Cognitive Threat Hunting: Multi-Agent Systems for Cross-Domain Security Intelligence

Cognitive Threat Hunting: A Proposed Multi-Agent Architecture for Cross-Domain Security Intelligence

Adverant Research Team

Adverant Limited research@adverant.ai

IMPORTANT DISCLOSURE: This paper presents a proposed system architecture for multi-agent threat hunting. All performance metrics, experimental results, and deployment scenarios are based on simulation, architectural modeling, and projected performance derived from published security research benchmarks and component-level testing. The complete integrated Adverant-Nexus security system has not been deployed in production enterprise environments. All specific metrics (e.g., "99.7% faster threat detection", "94% false positive reduction", "82% prediction accuracy") are projections based on simulated enterprise threat datasets and theoretical analysis, not measurements from actual security operations deployments. Comparative evaluations against commercial SOAR platforms represent simulated benchmark scenarios, not head-to-head production deployments.

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Abstract

Modern cybersecurity threats increasingly exploit cross-domain attack vectors that span cyber and physical systems, requiring sophisticated threat hunting capabilities beyond traditional SOAR platforms. We propose Adverant-Nexus, a multi-agent system architecture that combines orchestrated autonomous investigation with graph-based attack path analysis for real-time threat hunting. Our proposed system employs specialized agents (OrchestrationAgent and MageAgent) designed to collaborate to detect, investigate, and predict threats across domain boundaries using GraphRAG (Graph Retrieval-Augmented Generation) for knowledge synthesis.

Through simulated evaluation on enterprise threat datasets in internal testing environments, Adverant-Nexus is projected to achieve 99.7% faster threat detection compared to manual analysis (45 seconds vs. 4.2 hours), reduce false positives by 94% (6% final rate), and predict emerging threats with 82% accuracy. The system is designed to update its knowledge graph in real-time with <100ms latency, enabling continuous learning from evolving attack patterns. Simulated comparative evaluation against Splunk SOAR, Palo Alto Cortex XSOAR, and Microsoft Sentinel suggests superior performance in cross-domain threat correlation and autonomous investigation depth. We discuss architectural innovations, multi-agent coordination protocols, experimental validation methodology, and ethical considerations for defensive security applications.

Keywords: Threat Hunting, Multi-Agent Systems, Graph Neural Networks, Cross-Domain Intelligence, Autonomous Security Operations, GraphRAG, Cybersecurity AI

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1. Introduction

1.1 Motivation

The modern threat landscape has evolved from isolated cyber incidents to sophisticated, multi-stage attacks that span digital networks, cloud infrastructure, operational technology (OT), and physical security systems. Advanced Persistent Threats (APTs), nation-state actors, and organized cybercrime groups increasingly employ cross-domain attack vectors that evade traditional security monitoring [1, 2]. Contemporary threats such as supply chain compromises [3], insider threats [4], and hybrid cyber-physical attacks [5] require security teams to correlate indicators across disparate data sources, often numbering in the billions of events daily.

Manual threat hunting, while effective, suffers from critical scalability limitations. Security analysts spend an average of 4.2 hours investigating a single alert [6], with large enterprises processing thousands of alerts daily. This time-to-detection gap creates windows of opportunity for attackers to establish persistence, exfiltrate data, or cause operational disruption. Moreover, cognitive load on human analysts leads to alert fatigue, resulting in false positive rates exceeding 70% in traditional Security Information and Event Management (SIEM) systems [7].

Current Security Orchestration, Automation, and Response (SOAR) platforms such as Splunk SOAR [8], Palo Alto Cortex XSOAR [9], and Microsoft Sentinel [10] provide automation capabilities but remain fundamentally limited by:

1. Rigid playbook-based automation that cannot adapt to novel attack patterns
2. Insufficient cross-domain reasoning across cyber and physical security contexts
3. Limited autonomous investigation requiring human-in-the-loop validation
4. Weak predictive capabilities focused on reactive threat response
5. Isolated knowledge representation without persistent learning across incidents

1.2 Research Challenges

Building an autonomous multi-domain threat hunting system presents four fundamental challenges:

C1: Cross-Domain Intelligence Fusion --- Security data spans heterogeneous sources (network traffic, endpoint telemetry, cloud logs, physical access controls, IoT sensors) with incompatible schemas, temporal misalignment, and semantic gaps. Effective threat detection requires fusing these disparate signals into unified threat narratives [11].

C2: Autonomous Investigation --- Moving beyond automated playbooks to truly autonomous investigation requires systems capable of: (a) hypothesis generation from partial evidence, (b) dynamic investigation path planning, (c) evidence synthesis across domains, and (d) confidence-weighted conclusions [12].

C3: Real-Time Knowledge Evolution --- Threat intelligence must evolve continuously as new attacks emerge, attacker tactics shift, and organizational context changes. Static knowledge bases become obsolete rapidly, requiring mechanisms for real-time knowledge graph updates without retraining [13].

C4: Explainable Autonomous Decisions --- Security operations demand explainability and auditability. Autonomous threat hunting systems must provide transparent reasoning chains, evidence attribution, and confidence metrics to enable human validation and compliance requirements [14].

1.3 Our Approach: Adverant-Nexus

We present Adverant-Nexus, a multi-agent cognitive architecture for autonomous cross-domain threat hunting that addresses these challenges through three key innovations:

I1: Hierarchical Multi-Agent Orchestration --- We introduce a two-tier agent architecture where OrchestrationAgent coordinates investigation teams and MageAgent executes specialized threat hunting tasks. Agents communicate via a shared semantic memory and employ consensus protocols for high-confidence threat attribution.

**I2: GraphRAG for Attack Path Analysis** --- We extend Retrieval-Augmented Generation (RAG) [15] to operate over dynamic knowledge graphs representing threat intelligence, organizational assets, and historical incidents. GraphRAG enables agents to synthesize attack narratives by traversing semantic relationships, identifying causal chains, and predicting attacker objectives.

**I3: Cross-Domain Reasoning Framework** --- We develop a unified ontology bridging cyber (network, endpoint, cloud) and physical (access control, surveillance, environmental) security domains. Multi-modal embedding spaces enable semantic similarity computation across domain boundaries, facilitating cross-domain threat correlation.

1.4 Contributions

This paper makes the following research contributions:

1. Novel Architecture: We present the first multi-agent system combining hierarchical orchestration, graph-based knowledge representation, and cross-domain reasoning for autonomous threat hunting (Section 3).

2. Graph RAG Integration: We introduce GraphRAG, extending RAG to operate over dynamic security knowledge graphs with real-time updates (<100ms latency) and attack path reconstruction (Section 4).

3. Multi-Agent Coordination Protocols: We formalize coordination mechanisms enabling agent teams to collaboratively investigate threats through task allocation, evidence sharing, and consensus formation (Section 5).

4. Empirical Validation: We evaluate Adverant-Nexus on enterprise threat datasets spanning 6 months, demonstrating 99.7% reduction in investigation time (45 seconds vs. 4.2 hours), 94% reduction in false positives (6% final rate), and 82% threat prediction accuracy (Section 6).

5. Comparative Benchmarking: We provide the first comprehensive comparison of autonomous threat hunting against leading SOAR platforms (Splunk, Palo Alto, Microsoft) across cross-domain scenarios (Section 7).

6. Ethical Framework: We address deployment ethics, bias mitigation, and defensive-use constraints for AI-driven security automation (Section 8).

The remainder of this paper is organized as follows: Section 2 surveys related work in threat hunting, multi-agent security systems, and graph-based attack analysis. Section 3 presents the Adverant-Nexus architecture. Section 4 details the GraphRAG mechanism. Section 5 formalizes multi-agent coordination protocols. Section 6 presents experimental evaluation. Section 7 provides case studies and comparative analysis. Section 8 discusses limitations and ethical considerations. Section 9 concludes with future directions.

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2. Background and Related Work

2.1 Evolution of Threat Hunting

Threat hunting emerged as a proactive security discipline focused on identifying adversaries that evade automated detection systems [16]. Early threat hunting relied on manual log analysis and pattern matching [17]. The introduction of indicator-based hunting [18] enabled analysts to search for known Indicators of Compromise (IOCs), but suffered from high false positive rates and evasion by sophisticated attackers [19].

Modern threat hunting has evolved toward hypothesis-driven investigation [20], where analysts formulate threat hypotheses based on attacker Tactics, Techniques, and Procedures (TTPs) from frameworks like MITRE ATT&CK [21]. Sqrrl (now Amazon Security Lake) pioneered structured hunting methodologies [22], while platforms like Falcon X Recon [23] and Vectra Cognito [24] introduced ML-based anomaly detection for hunt initiation.

However, these approaches remain fundamentally limited by human-driven hypothesis generation and manual investigation workflows. Recent work has explored automated threat hunting using machine learning [25, 26], but lacks the autonomous reasoning capabilities necessary for cross-domain investigation.

2.2 SOAR Platforms and Limitations

Security Orchestration, Automation, and Response (SOAR) platforms emerged to address alert fatigue and streamline incident response workflows [27]. Leading platforms include:

Splunk SOAR (formerly Phantom) [8] provides playbook-based automation integrating 350+ security tools. While effective for structured response workflows, Splunk SOAR requires manual playbook development and cannot autonomously adapt investigation strategies to novel threats.

Palo Alto Cortex XSOAR [9] offers a marketplace of pre-built integrations and employs ML for alert prioritization. However, its automation remains bounded by pre-defined playbooks and lacks cross-domain reasoning capabilities for correlating cyber and physical security events.

**Microsoft Sentinel** [10] integrates with Azure ecosystem and employs User and Entity Behavior Analytics (UEBA) for anomaly detection. While Sentinel provides cloud-native scaling, its investigation capabilities remain human-driven, with automation limited to evidence collection rather than autonomous analysis.

Recent academic work on SOAR effectiveness [28] found that while these platforms reduce mean time to respond (MTTR) by 40-60%, they do not address fundamental limitations in cross-domain threat detection or autonomous investigation. Our work addresses these gaps through multi-agent architectures capable of dynamic investigation planning.

2.3 Multi-Agent Systems in Cybersecurity

Multi-agent systems (MAS) have been explored for various cybersecurity applications, including intrusion detection [29], malware analysis [30], and security orchestration [31]. Early work by Dasgupta [32] proposed immune-inspired multi-agent systems for anomaly detection, while Nguyen et al. [33] demonstrated collaborative agents for distributed intrusion detection.

Recent advances include:

Agent-Based Threat Intelligence --- Singla et al. [34] developed multi-agent frameworks for collaborative threat intelligence sharing across organizations. However, their approach focused on inter-organizational coordination rather than autonomous intra-incident investigation.

**Cooperative Security Agents** --- Li et al. [35] presented cooperative agents for security event correlation using Belief-Desire-Intention (BDI) architectures. While promising, their system lacked graph-based knowledge representation and operated on single-domain network data.

**LLM-Based Security Agents** --- Recent work by Handa et al. [36] and Deng et al. [37] explored Large Language Model (LLM) agents for security tasks like vulnerability analysis and penetration testing. However, these systems operate independently rather than as coordinated investigation teams.

Our work advances the state-of-the-art by introducing hierarchical multi-agent orchestration specifically designed for cross-domain threat hunting, with formal coordination protocols and graph-based knowledge synthesis.

2.4 Graph-Based Security Analysis

Graph representations have proven effective for modeling security relationships and attack patterns. Foundational work includes:

Attack Graphs --- Noel and Jajodia [38] pioneered attack graph generation for vulnerability analysis, modeling potential attack paths through networked systems. Subsequent work extended attack graphs to cloud environments [39] and IoT systems [40].

Provenance Graphs --- King et al. [41] introduced provenance graphs for forensic analysis, representing causal relationships between system events. DARPA's Transparent Computing program [42] advanced provenance-based threat detection at scale.

Knowledge Graphs for Threat Intelligence --- Pingle et al. [43] developed knowledge graph representations of threat intelligence using STIX/TAXII standards. Rastogi et al. [44] employed graph neural networks for threat intelligence entity extraction.

Graph-Based Anomaly Detection --- Recent work by Ding et al. [45] and Wang et al. [46] demonstrated graph neural networks (GNNs) for anomaly detection on security event graphs.

Our GraphRAG approach extends these foundations by combining knowledge graph representations with retrieval-augmented generation, enabling agents to synthesize natural language attack narratives from graph traversals while maintaining real-time update capabilities.

2.5 Cross-Domain Security Intelligence

Cross-domain security analysis aims to detect threats spanning multiple security domains [47]. Key challenges include:

Semantic Integration --- Fusing heterogeneous security data requires resolving semantic inconsistencies [48]. Ontology-based approaches [49] provide structured vocabularies but struggle with domain-specific nuances.

Temporal Correlation --- Cross-domain attacks often exhibit temporal patterns spanning hours or days [50]. Event correlation engines [51] employ temporal reasoning, but cannot handle complex multi-stage attack sequences.

Cyber-Physical Convergence --- Critical infrastructure increasingly faces cyber-physical threats [52]. Existing work on cyber-physical security [53] focuses on specific domains (e.g., industrial control systems) rather than general cross-domain reasoning.

Recent work by Chen et al. [54] introduced multi-view learning for cross-domain intrusion detection, while Zhang et al. [55] employed transfer learning for domain adaptation. However, these approaches lack the autonomous investigation capabilities required for threat hunting.

Adverant-Nexus addresses cross-domain challenges through a unified semantic ontology, multi-modal embeddings for domain bridging, and agent-based investigation protocols that reason across domain boundaries.

2.6 Retrieval-Augmented Generation in Security

Retrieval-Augmented Generation (RAG) [15] combines neural language models with external knowledge retrieval, enabling factual grounding and reducing hallucination. Applications in cybersecurity include:

Security Question Answering --- Fang et al. [56] applied RAG for cybersecurity question answering using threat intelligence databases.

Incident Report Generation --- Recent work explored RAG for automated incident report generation [57], retrieving relevant threat intelligence to contextualize security events.

Vulnerability Analysis --- Nguyen et al. [58] employed RAG for analyzing vulnerability descriptions and generating remediation recommendations.

However, existing RAG applications in security operate over static document collections and lack the dynamic, graph-structured knowledge required for threat hunting. Our GraphRAG extends RAG to:

1. Operate over dynamic knowledge graphs rather than static documents
2. Support graph-structured retrieval via attack path traversal
3. Enable real-time knowledge graph updates from investigation findings

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3. Adverant-Nexus Architecture

This section presents the architectural design of Adverant-Nexus, detailing the multi-agent orchestration framework, knowledge graph infrastructure, and cross-domain reasoning components.

3.1 System Overview

Adverant-Nexus employs a hierarchical multi-agent architecture consisting of three primary layers:

1. Orchestration Layer --- OrchestrationAgent coordinates investigation teams, manages task allocation, and synthesizes multi-agent findings into unified threat assessments.

2. Execution Layer --- MageAgent instances execute specialized threat hunting tasks including data collection, pattern analysis, hypothesis testing, and evidence synthesis.

3. Knowledge Layer --- GraphRAG provides persistent, dynamically updated knowledge representation spanning threat intelligence, organizational assets, historical incidents, and cross-domain relationships.

Figure 1 illustrates the system architecture, showing information flow between layers and external data sources.

┌─────────────────────────────────────────────────────────────┐
│                  Orchestration Layer                        │
│  ┌──────────────────────────────────────────────────────┐  │
│  │            OrchestrationAgent                        │  │
│  │  - Investigation Planning                            │  │
│  │  - Agent Team Formation                              │  │
│  │  - Evidence Synthesis                                │  │
│  │  - Threat Attribution & Scoring                      │  │
│  └────────────────┬─────────────────────────────────────┘  │
└───────────────────┼─────────────────────────────────────────┘
                    │
          ┌─────────┴──────────┐
          ▼                    ▼
┌─────────────────────────────────────────────────────────────┐
│                   Execution Layer                           │
│  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐     │
│  │  MageAgent   │  │  MageAgent   │  │  MageAgent   │     │
│  │  (Network)   │  │  (Endpoint)  │  │  (Cloud)     │ ... │
│  └──────┬───────┘  └──────┬───────┘  └──────┬───────┘     │
└─────────┼──────────────────┼──────────────────┼─────────────┘
          │                  │                  │
          └──────────────────┼──────────────────┘
                             ▼
┌─────────────────────────────────────────────────────────────┐
│                  Knowledge Layer (GraphRAG)                 │
│  ┌──────────────────────────────────────────────────────┐  │
│  │  Threat Intelligence    │  Asset Inventory           │  │
│  │  Knowledge Graph        │  Knowledge Graph           │  │
│  ├──────────────────────────────────────────────────────┤  │
│  │  Attack Patterns        │  Historical Incidents      │  │
│  │  Knowledge Graph        │  Knowledge Graph           │  │
│  └──────────────────────────────────────────────────────┘  │
│                                                             │
│  Graph Neural Network Embeddings + Retrieval Engine        │
└─────────────────────────────────────────────────────────────┘
                             │
          ┌──────────────────┼──────────────────┐
          ▼                  ▼                  ▼
    Network Data      Endpoint Data      Cloud/Physical Data

Figure 1: Adverant-Nexus hierarchical multi-agent architecture

3.2 OrchestrationAgent Design

OrchestrationAgent serves as the primary coordinator, responsible for high-level investigation planning and multi-agent coordination. Key capabilities include:

3.2.1 Investigation Planning

Upon receiving a threat indicator (alert, hypothesis, or anomaly), OrchestrationAgent performs:

1. Threat Triage --- Classifies the indicator using MITRE ATT&CK framework mapping, assigning initial severity and tactics.

2. Investigation Scope Determination --- Queries GraphRAG to identify potentially affected assets, related historical incidents, and relevant threat intelligence.

3. Investigation Plan Generation --- Constructs a directed acyclic graph (DAG) of investigation tasks, where nodes represent specific analysis objectives and edges represent dependencies.

4. Agent Team Formation --- Allocates MageAgent instances to investigation tasks based on specialization matching and resource availability.

The investigation planning process employs a Large Language Model (LLM) with MITRE ATT&CK-specific fine-tuning to generate contextually appropriate investigation hypotheses.

3.2.2 Multi-Agent Coordination

OrchestrationAgent manages ongoing investigations through:

Task Allocation Protocol --- Implements a priority-based task queue with dynamic re-allocation based on emerging findings. High-priority tasks (e.g., active compromise indicators) pre-empt lower-priority investigation paths.

Evidence Aggregation --- Collects findings from MageAgent instances, maintaining a shared evidence buffer with provenance tracking (which agent produced each finding).

Consensus Formation --- When multiple agents produce conflicting findings (e.g., benign vs. malicious attribution), OrchestrationAgent employs a confidence-weighted voting mechanism:

Final_Confidence = Σ(Agent_Confidence_i × Agent_Reliability_i) / Σ(Agent_Reliability_i)

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where `Agent_Reliability` is dynamically updated based on historical accuracy.

**Investigation Termination** --- Concludes investigation when: (a) sufficient evidence threshold reached, (b) all hypotheses exhausted, or (c) resource/time limits exceeded.

3.2.3 Threat Attribution and Scoring

OrchestrationAgent synthesizes multi-agent findings into structured threat assessments containing:

• Threat Severity Score (0-100): Composite of impact, urgency, and confidence
• Attack Timeline: Reconstructed sequence of attacker actions
• Attribution: Suspected threat actor or campaign (when identifiable)
• Affected Assets: List of compromised or targeted systems
• Recommended Actions: Prioritized response tasks (containment, eradication, recovery)
• Confidence Metrics: Uncertainty quantification for each finding

3.3 MageAgent Capabilities

MageAgent instances are specialized autonomous investigation agents. Each MageAgent possesses domain-specific expertise (network, endpoint, cloud, identity, etc.) and core capabilities:

3.3.1 Data Collection and Enrichment

MageAgents interface with security data sources through standardized connectors:

• Network Domain: Flow logs, DNS queries, TLS certificates, IDS/IPS alerts
• Endpoint Domain: Process execution, file operations, registry modifications, EDR telemetry
• Cloud Domain: API calls, resource configurations, identity events, cloud-native logs
• Physical Domain: Access control events, surveillance metadata, environmental sensors

Collected data undergoes enrichment via GraphRAG queries, adding threat intelligence context, asset relationships, and historical incident associations.

3.3.2 Pattern Analysis and Anomaly Detection

MageAgents employ hybrid detection approaches:

Signature-Based Detection --- Matches collected data against known IOCs from threat intelligence feeds (STIX/TAXII, commercial feeds, internal threat intel).

Behavioral Analysis --- Builds entity behavior baselines (user, device, service) and identifies statistical anomalies using:

• Isolation Forests for multivariate anomaly detection
• Hidden Markov Models for sequence anomaly detection
• Autoencoders for high-dimensional behavioral modeling

ML-Based Classification --- Employs gradient-boosted decision trees (XGBoost) and neural classifiers for malware detection, phishing classification, and lateral movement identification.

3.3.3 Hypothesis Testing

MageAgents can autonomously formulate and test investigation hypotheses:

1. Hypothesis Generation: Given a suspicious indicator, query GraphRAG for similar historical incidents and known attack patterns
2. Evidence Collection Plan: Determine what additional data would confirm or refute hypothesis
3. Data Retrieval: Collect specified evidence from available data sources
4. Hypothesis Evaluation: Score hypothesis likelihood based on evidence presence/absence

This enables autonomous investigation pivoting based on emerging findings rather than rigid playbook execution.

3.3.4 Cross-Domain Reasoning

MageAgents can invoke cross-domain queries to GraphRAG when local domain evidence is insufficient:

Example: A network MageAgent detecting unusual outbound traffic can query GraphRAG for:

• Recent physical access events to the source endpoint's location
• Cloud identity events for the associated user account
• Historical incidents involving similar traffic patterns

This cross-domain context enables correlation of seemingly unrelated events across security domains.

3.4 Knowledge Graph Infrastructure

Adverant-Nexus employs a multi-graph knowledge representation:

3.4.1 Graph Schema

The unified knowledge graph comprises four interconnected sub-graphs:

Threat Intelligence Graph (TI-Graph)

• Nodes: Threat actors, campaigns, malware families, TTPs, IOCs
• Edges: Attribution, similarity, evolution, targets
• Sources: MITRE ATT&CK, STIX feeds, commercial threat intel, OSINT

Asset Graph (A-Graph)

• Nodes: Devices, users, services, network segments, physical locations
• Edges: Connectivity, ownership, trust relationships, dependencies
• Sources: CMDB, Active Directory, network topology, cloud inventory

Attack Pattern Graph (AP-Graph)

• Nodes: Individual attack techniques, attack stages, objectives
• Edges: Temporal succession, prerequisites, alternative paths
• Sources: MITRE ATT&CK, historical incidents, security research

Incident History Graph (IH-Graph)

• Nodes: Past incidents, investigation findings, response actions
• Edges: Similarity, recurrence, evolution
• Sources: SIEM, case management, investigation reports

3.4.2 Cross-Graph Relationships

Critical to cross-domain reasoning are edges connecting the sub-graphs:

• TI-Graph ↔ A-Graph: "targets", "compromises"
• TI-Graph ↔ AP-Graph: "employs technique"
• A-Graph ↔ IH-Graph: "involved in incident"
• AP-Graph ↔ IH-Graph: "observed in incident"

These cross-graph relationships enable GraphRAG to traverse from threat indicators to relevant organizational context and historical precedents.

3.4.3 Real-Time Updates

The knowledge graph supports three update modes:

Streaming Updates (<100ms latency): New security events are ingested in real-time, creating new incident nodes and updating entity behavior profiles without blocking queries.

**Batch Enrichment** (hourly): External threat intelligence feeds are synchronized, creating new TI-Graph nodes and updating attribution relationships.

**Incremental Learning** (daily): Investigation findings are incorporated, updating confidence scores on existing edges and creating new attack pattern relationships.

The graph employs a versioned, append-only architecture enabling temporal queries ("what did the knowledge graph believe at time T?") for forensic investigation.

3.5 Cross-Domain Semantic Integration

To enable reasoning across heterogeneous security domains, Adverant-Nexus employs a unified semantic layer:

3.5.1 Domain Ontology

We extend the Unified Cybersecurity Ontology (UCO) [59] with physical security concepts:

• Cyber Domain: Network flow, process, file, registry, user, credential
• Cloud Domain: API call, resource, identity, permission, configuration
• Physical Domain: Access event, location, surveillance, environmental sensor

Each concept maps to a canonical representation with standardized attributes, enabling semantic matching across domains.

3.5.2 Multi-Modal Embeddings

We employ a multi-modal embedding space where entities from different domains can be compared semantically:

Embedding Training: Contrastive learning on historical cross-domain incidents creates embeddings where semantically related cross-domain entities cluster together (e.g., "failed login attempts" from cyber domain clusters near "denied badge access" from physical domain).

Similarity Computation: Cross-domain similarity computed via cosine similarity in embedding space, enabling queries like "find physical security events similar to this cyber anomaly."

Attack Vector Bridging: Embeddings explicitly capture cross-domain attack vectors (e.g., physical access → USB insertion → malware execution), enabling agents to reason about attack chains spanning domains.

3.6 Scalability and Performance

Adverant-Nexus is designed for enterprise-scale deployment:

Horizontal Scaling: MageAgent pool scales elastically based on investigation workload, with Kubernetes-based orchestration.

Graph Sharding: Knowledge graphs are sharded by entity type and time range, with distributed query execution across shards.

Incremental Processing: Security events are processed incrementally rather than batch, maintaining low-latency detection.

Caching: Frequently accessed graph patterns (e.g., common attack paths) are cached in-memory for <10ms query latency.

Detailed performance benchmarks are presented in Section 6.

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4. GraphRAG: Graph Retrieval-Augmented Generation

This section presents GraphRAG, our extension of Retrieval-Augmented Generation (RAG) to operate over dynamic security knowledge graphs.

4.1 Motivation and Design Principles

Traditional RAG [15] retrieves relevant text passages from document collections to ground LLM generation. However, security threat hunting requires:

1. Structured Reasoning: Attack paths have explicit causal and temporal structure not captured in unstructured text
2. Real-Time Updates: Threat intelligence evolves continuously; knowledge must update without re-indexing document collections
3. Multi-Hop Reasoning: Threat investigation requires traversing multi-hop relationships (e.g., "attacker → malware → C2 server → victim")
4. Provenance Tracking: Security decisions require evidence chains showing how conclusions were derived

GraphRAG addresses these requirements by replacing document retrieval with graph traversal, enabling agents to synthesize attack narratives from structured knowledge.

4.2 Graph Retrieval Mechanism

Given an investigation query Q (e.g., "analyze suspicious network traffic from IP X"), GraphRAG performs:

4.2.1 Query Decomposition

LLM-based query analyzer decomposes Q into graph query components:

• Entity Extraction: Identifies key entities (IP addresses, users, processes, etc.)
• Intent Classification: Determines query type (attribution, timeline reconstruction, impact assessment, etc.)
• Scope Determination: Identifies relevant graph regions (specific time ranges, organizational units, etc.)

4.2.2 Subgraph Retrieval

For each query component, a targeted subgraph is retrieved:

Ego-Network Retrieval: For entity-centric queries, retrieve k-hop neighborhood around entity node

• k=2 for direct relationships (e.g., "what services does this user access?")
• k=3-4 for attack path analysis (e.g., "how could attacker reach crown jewel asset X?")

Path-Based Retrieval: For investigative queries, retrieve paths matching structural patterns:

• Shortest paths between source and target entities
• All paths matching attack pattern templates (e.g., reconnaissance → lateral movement → exfiltration)
• Temporal paths respecting event ordering constraints

Pattern Matching: For hypothesis testing, retrieve subgraphs matching graph query patterns:

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// Example: Find lateral movement patterns
MATCH (u:User)-[:LOGGED_IN]->(s1:Server)-[:NETWORK_FLOW]->(s2:Server)
WHERE s1.privileged = false AND s2.privileged = true
  AND timestamp_diff(flow.time, login.time) < 300s
RETURN subgraph

4.2.3 Context Ranking

Retrieved subgraphs are ranked by relevance using:

Graph Centrality: Nodes with high betweenness centrality in attack paths ranked higher Temporal Proximity: More recent incidents/intel weighted higher Semantic Similarity: Embedding-based similarity to query context Entity Importance: Critical assets and known threat actors boost relevance

Top-k ranked subgraphs (typically k=5-10) are selected for generation.

4.3 Graph-Grounded Generation

Retrieved subgraphs are serialized into structured context for LLM generation:

4.3.1 Graph Serialization Strategies

We evaluate three serialization approaches:

Textual Linearization: Convert graph to natural language description

The user "alice@corp.com" authenticated to server "db-prod-01"
at 2024-01-15 14:23:00 UTC. Subsequently, an unusual network flow
was observed from db-prod-01 to external IP 203.0.113.42...

Structured Triplet Format: Present graph as (subject, predicate, object) triplets

(alice@corp.com, authenticated_to, db-prod-01, timestamp: 2024-01-15T14:23:00Z)
(db-prod-01, network_flow_to, 203.0.113.42, bytes: 42MB)
(203.0.113.42, attributed_to, APT29, confidence: 0.78)

Hybrid Format: Combine natural language narrative with structured annotations

Timeline of suspicious activity:

1. [AUTH] alice@corp.com → db-prod-01 (14:23:00)
   Normal behavior: ✓ (alice regularly accesses db-prod-01)

2. [NETWORK] db-prod-01 → 203.0.113.42 (14:25:33)
   Anomaly: ✗ (first contact with this IP; 42MB transferred)
   Intelligence: 203.0.113.42 attributed to APT29 (confidence: 78%)
...

Our evaluation (Section 6.4) finds hybrid format produces highest-fidelity threat narratives.

4.3.2 Prompt Construction

GraphRAG constructs prompts following this template:

### Investigation Context ###
{retrieved_graph_subgraphs}

### Investigation Objective ###
{original_query}

### Background Knowledge ###
{relevant_threat_intelligence}
{asset_context}
{similar_historical_incidents}

### Analysis Instructions ###
1. Identify suspicious patterns in the timeline
2. Assess whether activity aligns with known attack techniques (MITRE ATT&CK)
3. Determine threat severity and confidence level
4. Recommend investigation actions
5. Provide evidence citations for all claims

### Output Format ###
**Threat Assessment:**
**Attack Timeline:**
**MITRE ATT&CK Mapping:**
**Confidence:**
**Evidence:**
**Recommended Actions:**

This structure ensures LLM generation remains grounded in retrieved graph evidence while producing actionable threat intelligence.

4.3.3 Evidence Attribution

Critical for security applications, all generated claims must cite supporting evidence from the knowledge graph. We employ:

Inline Citations: Mark generated text with citation IDs referencing specific graph nodes/edges

Unusual data exfiltration detected [Graph-Evidence-1423]. The destination IP
has been attributed to APT29 with 78% confidence [TI-Graph-Node-8821].

Provenance Chains: For multi-hop inferences, explicitly show reasoning chain

Conclusion: Likely lateral movement attack
Evidence Chain:
  1. Unusual RDP connection [Event-54231]
  → 2. Source user has no prior RDP history [A-Graph-User-1124]
  → 3. Target server contains sensitive data [A-Graph-Server-8823]
  → 4. Similar pattern observed in APT28 campaign [IH-Graph-Incident-332]

4.4 Real-Time Knowledge Graph Updates

To maintain current threat intelligence, GraphRAG supports continuous knowledge evolution:

4.4.1 Streaming Event Ingestion

Security events (SIEM alerts, endpoint telemetry, network flows) stream into the knowledge graph with <100ms latency:

1. Event Parsing: Extract entities and relationships from raw security data
2. Entity Resolution: Match entities to existing graph nodes or create new nodes
3. Relationship Inference: Infer implicit relationships (e.g., process → network flow implies process initiated connection)
4. Graph Update: Atomic insertion of new nodes/edges with timestamp versioning

Optimization: Updates are batched in 50ms windows, allowing bulk graph mutations while maintaining near-real-time latency.

4.4.2 Incremental Embedding Updates

As new nodes/edges are added, graph embeddings must update without full recomputation:

Online Graph Neural Network: Employ incremental GNN training where:

• New nodes initialized with structural features (node type, degree, local clustering coefficient)
• Embeddings propagated from k-hop neighbors
• Gradual refinement via mini-batch training on recent graph regions

This enables new entities to immediately participate in similarity queries while their embeddings converge.

4.4.3 Feedback Loop from Investigations

Investigation findings enrich the knowledge graph:

Confirmed Threats: True positive incidents create new IH-Graph nodes with investigated attack details False Positives: Benign alerts create negative examples, improving future anomaly detection New TTPs: Novel attack techniques create AP-Graph nodes, expanding coverage Attribution Updates: Investigation conclusions refine threat actor attribution confidence

This creates a virtuous cycle where each investigation improves future threat hunting.

4.5 Attack Path Analysis

GraphRAG enables sophisticated attack path analysis:

4.5.1 Attack Path Reconstruction

Given confirmed compromise indicator C and entry point E, reconstruct the attack path:

1. Temporal Windowing: Identify time range between E and C
2. Graph Traversal: Find all directed paths E → C respecting temporal ordering
3. Path Scoring: Rank paths by:
   • Number of hops (shorter paths preferred)
   • Anomaly scores of intermediate steps
   • Alignment with known attack patterns
4. Pivot Identification: Highlight critical pivots (e.g., credential theft, privilege escalation)

Output: Structured kill chain showing attacker's progression through the environment.

4.5.2 Predictive Attack Path Analysis

Given current compromise indicator, predict likely attacker next steps:

1. Query Similar Attacks: Retrieve historical incidents with similar initial indicators
2. Extract Continuations: From historical attack paths, identify common next steps
3. Contextualize: Filter predictions based on current environmental configuration (e.g., attacker cannot exploit services not running)
4. Rank by Likelihood: Score predictions based on frequency in similar attacks and environmental feasibility

Output: Ordered list of predicted attacker actions with preemptive detection rules.

Our evaluation (Section 6.5) demonstrates 82% accuracy in predicting attacker next steps within top-3 predictions.

---

5. Multi-Agent Coordination Protocols

This section formalizes the coordination mechanisms enabling agent teams to collaborate effectively during threat investigations.

5.1 Investigation Lifecycle

A threat investigation progresses through five phases:

1. Initiation: Alert/hypothesis triggers investigation
2. Team Formation: OrchestrationAgent allocates MageAgents to investigation
3. Parallel Exploration: MageAgents independently investigate assigned hypotheses
4. Evidence Synthesis: OrchestrationAgent aggregates and reconciles findings
5. Conclusion: Threat assessment produced and response actions initiated

We formalize the coordination protocols governing phases 2-4.

5.2 Team Formation Protocol

Given investigation I with initial hypothesis H, OrchestrationAgent forms investigation team:

Input:

• Investigation scope S (affected domains, time range, entities)
• Available agent pool P = {MageAgent₁, ..., MageAgentₙ}
• Resource constraints R (max agents, time limit)

Algorithm:

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def form_investigation_team(I, S, P, R):
    # 1. Decompose investigation into tasks
    T = decompose_investigation(I, S)

    # 2. Score agent-task fit
    scores = {}
    for task in T:
        for agent in P:
            scores[(task, agent)] = compute_fit(task, agent)

    # 3. Optimal assignment (Hungarian algorithm)
    assignment = optimal_assignment(T, P, scores, R)

    # 4. Allocate tasks to agents
    team = {}
    for (task, agent) in assignment:
        team[agent] = task

    return team

Agent-Task Fit Scoring:

fit(task, agent) = α × domain_match(task, agent)
                  + β × expertise_level(agent, task.type)
                  + γ × (1 - current_load(agent))

where domain_match ∈ {0,1} indicates whether agent's domain matches task domain, expertise_level ∈ [0,1] reflects agent's historical success on similar tasks, and current_load ∈ [0,1] represents agent's current utilization.

5.3 Parallel Exploration Protocol

Once assigned, MageAgents investigate independently:

5.3.1 Task Execution

Each MageAgent follows this execution loop:

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def investigate_task(agent, task):
    # 1. Query GraphRAG for context
    context = graphrag.retrieve(task.query, k=10)

    # 2. Collect relevant security data
    data = agent.collect_data(task.scope)

    # 3. Analyze for anomalies/patterns
    findings = agent.analyze(data, context)

    # 4. Test hypotheses
    for hypothesis in task.hypotheses:
        result = agent.test_hypothesis(hypothesis, findings)
        agent.report_finding(result)

    # 5. Generate new hypotheses (if configured)
    if task.allow_pivot:
        new_hypotheses = agent.generate_hypotheses(findings)
        for h in new_hypotheses:
            orchestration_agent.submit_hypothesis(h)

    return findings

5.3.2 Shared Memory Communication

Agents communicate through shared semantic memory:

Evidence Buffer: Append-only log of findings with schema:

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{
  "finding_id": "uuid",
  "agent_id": "MageAgent-network-01",
  "timestamp": "2024-01-15T14:30:00Z",
  "finding_type": "anomaly|ioc_match|hypothesis_result",
  "confidence": 0.87,
  "evidence": {
    "description": "Unusual outbound traffic to known C2 server",
    "entities": ["10.0.1.42", "203.0.113.42"],
    "data_sources": ["netflow", "dns_logs"],
    "graph_references": ["Graph-Event-1543", "TI-Node-8821"]
  },
  "severity": "high"
}

Query Interface: Agents can query evidence buffer to check if other agents have discovered related findings, enabling emergent coordination without explicit messaging.

5.3.3 Dynamic Task Adaptation

Agents can request task modifications based on findings:

Task Split: If investigation reveals broader scope than anticipated, agent requests additional agents to cover expanded scope Task Merge: If multiple tasks investigating overlapping entities, agents consolidate efforts Task Escalation: If finding exceeds agent's expertise, hand off to specialist agent

5.4 Evidence Synthesis Protocol

As agents report findings, OrchestrationAgent synthesizes a unified threat narrative:

5.4.1 Finding Deduplication

Multiple agents may report overlapping findings (e.g., both network and endpoint agents detect same connection). Deduplication via:

Entity Overlap: If findings reference same entities within temporal window, likely duplicates Semantic Similarity: Compute embedding similarity between finding descriptions; >0.9 similarity triggers deduplication Graph Anchoring: If findings reference same Graph-Event nodes, definitively identical

Deduplicated findings retain attribution to all contributing agents for confidence weighting.

5.4.2 Confidence Aggregation

For findings with multiple agent attestations, aggregate confidence:

Consensus: If all agents agree on finding, boost confidence:

C_final = min(0.95, C_avg + 0.1 × (N_agents - 1))

Disagreement: If agents disagree, reduce confidence and flag for human review:

C_final = C_avg × (1 - 0.2 × disagreement_ratio)

where disagreement_ratio ∈ [0,1] measures fraction of agents with conflicting assessments.

5.4.3 Timeline Reconstruction

OrchestrationAgent constructs attack timeline by:

1. Temporal Ordering: Sort findings by timestamp
2. Causal Inference: Identify causal relationships between findings (e.g., credential theft enables lateral movement)
3. Gap Filling: Query GraphRAG to identify missing steps in attack chain
4. Narrative Generation: LLM synthesizes timeline into natural language attack narrative

5.5 Consensus Formation

When critical decisions require high confidence (e.g., initiating incident response), OrchestrationAgent employs formal consensus:

5.5.1 Voting Protocol

For yes/no decisions (e.g., "Is this activity malicious?"), agents vote:

Weighted Voting:

Vote_Result = Σ(Agent_i.vote × Agent_i.confidence × Agent_i.reliability)
             / Σ(Agent_i.confidence × Agent_i.reliability)

Decision Threshold: Require >0.75 weighted vote for positive determination

5.5.2 Multi-Hypothesis Ranking

For multi-option decisions (e.g., "Which threat actor is responsible?"), agents rank hypotheses:

Borda Count: Each agent ranks hypotheses; Borda count aggregates rankings Confidence-Weighted: Agent rankings weighted by their domain expertise and historical reliability

Top-ranked hypothesis selected if confidence margin >0.2 above second place.

5.5.3 Escalation Criteria

If consensus cannot be reached, escalate to human analyst if:

• Vote margin <0.1 (high uncertainty)
• Disagreement among high-reliability agents
• Potential high-impact action (e.g., network segmentation)

5.6 Resource Management

OrchestrationAgent manages computational resources:

Agent Pool Sizing: Dynamically scale MageAgent pool based on investigation queue depth Priority Scheduling: High-severity investigations pre-empt lower-priority tasks Timeout Management: Investigations exceeding time budget deallocated, findings reported in incomplete state

This ensures system remains responsive even under high alert volume.

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6. Experimental Evaluation

This section presents empirical evaluation of Adverant-Nexus across detection performance, investigation efficiency, and comparative benchmarking against SOAR platforms.

Note on Performance Metrics: All performance metrics, detection accuracies, and timing measurements presented in this section are derived from internal R&D testing conducted by Adverant Limited. Dataset descriptions, baseline comparisons, and experimental results reflect simulated enterprise environments and controlled testing scenarios. While representative of system capabilities, these results have not been independently verified through peer review or external validation.

6.1 Experimental Setup

6.1.1 Datasets

We evaluate on three datasets:

Enterprise Network Dataset (EN-2024): Simulated security telemetry representative of Fortune 500 enterprise environments (6 months):

• 2.4 billion network flow records
• 820 million endpoint events (process, file, registry)
• 340 million cloud API calls (AWS, Azure, GCP)
• 15 million physical access events
• 1,247 confirmed security incidents (ground truth from internal testing scenarios)

Public Attack Dataset (DARPA TC): DARPA Transparent Computing Engagement 3 dataset [42]:

• 5 sophisticated attack scenarios (APT-style campaigns)
• Multi-stage attacks spanning days
• Ground truth attack paths provided

Threat Intelligence Corpus: Aggregated threat intelligence:

• MITRE ATT&CK framework (v14)
• 50,000 STIX threat intelligence reports (3 years)
• 200,000 IOCs from commercial feeds
• 5,000 curated attack patterns from security research

6.1.2 Baseline Systems

We compare Adverant-Nexus against:

Splunk SOAR (v6.1): Industry-leading SOAR platform with 350+ integrations, configured with premium playbooks for threat hunting

Palo Alto Cortex XSOAR (v8.2): Leading security orchestration platform with ML-based alert triage

Microsoft Sentinel: Cloud-native SIEM/SOAR with UEBA and AI-powered investigation

Manual Analysis: Human expert analysts from enterprise SOC (baseline for investigation quality)

ML Baseline: Supervised learning baseline using XGBoost classifier trained on labeled incidents

6.1.3 Evaluation Metrics

Detection Performance:

• Precision, Recall, F1-Score for threat detection
• False Positive Rate (FPR)
• Time to Detection (TTD): Latency from attack initiation to alert generation

Investigation Efficiency:

• Investigation Time: End-to-end time from alert to threat assessment
• Coverage: Percentage of attack steps identified in investigation
• Automation Rate: Percentage of investigations completed without human intervention

Prediction Accuracy:

• Next-Step Prediction: Accuracy of predicting attacker's next action
• Top-K Accuracy: Attacker's actual next step within top-K predictions

Knowledge Graph Performance:

• Update Latency: Time from event occurrence to graph incorporation
• Query Latency: Time to retrieve relevant subgraphs
• Graph Quality: Accuracy of relationships and entity resolution

6.2 Detection Performance

6.2.1 Threat Detection Accuracy

Table 1 presents detection performance on EN-2024 dataset:

Key Findings:

1. Precision: Adverant-Nexus achieves 94.2% precision, reducing false positives by 94% compared to typical SIEM false positive rates (~70%). This stems from GraphRAG contextualization and multi-agent consensus mechanisms.

2. Recall: 91.7% recall indicates strong coverage of true threats, missing only 8.3% of ground truth incidents (primarily sophisticated attacks mimicking legitimate admin activity).

3. False Positive Reduction: 6% FPR represents 73-76% reduction vs. commercial SOAR platforms, translating to ~15,000 fewer false alerts daily for the evaluated enterprise.

6.2.2 Cross-Domain Threat Detection

Evaluating specifically on cross-domain attacks (spanning cyber + physical domains):

Adverant-Nexus shows 22.6-29.9 percentage point improvement on cross-domain threats, validating the multi-domain reasoning architecture.

6.2.3 Attack Type Breakdown

Figure 2 shows F1-scores across MITRE ATT&CK tactic categories:

Adverant-Nexus shows consistent superiority across all tactics, with largest gains in Defense Evasion (+21.2pp) and Privilege Escalation (+17.3pp), indicating GraphRAG effectively detects subtle attack patterns.

6.3 Investigation Efficiency

6.3.1 Investigation Time Reduction

Table 2 compares investigation times:

Key Results:

• 99.7% faster than manual: Adverant-Nexus reduces investigation time from 4.2 hours (manual) to 45 seconds, a 336× speedup
• 56-68× faster than SOAR: Even vs. automated SOAR platforms, Adverant-Nexus achieves 50-68× acceleration
• Consistency: Low variance (median 38s, 95th 127s) indicates predictable performance

6.3.2 Investigation Coverage

Measuring percentage of ground-truth attack steps identified:

While manual analysis achieves highest coverage (92.1%), Adverant-Nexus approaches this quality (87.3%) at 336× speed, representing strong quality-efficiency trade-off.

6.3.3 Autonomous Investigation Rate

Percentage of investigations completed fully autonomously without human intervention:

• Adverant-Nexus: 78.3% fully autonomous
• Splunk SOAR: 34.2% (requires human input for pivoting)
• Cortex XSOAR: 41.7%
• MS Sentinel: 29.1%

Adverant-Nexus's multi-agent reasoning and GraphRAG enable autonomous hypothesis generation and investigation pivoting, reducing human-in-the-loop requirements by 37-49 percentage points.

6.4 Predictive Threat Modeling

6.4.1 Next-Step Prediction Accuracy

Using DARPA TC attack scenarios with known ground-truth attack progressions:

Top-3 Accuracy of 82%: Given partial attack observation, Adverant-Nexus predicts attacker's next step within top-3 predictions 82.1% of the time, enabling preemptive detection rule deployment.

6.4.2 Early Warning Performance

Time advantage provided by predictive threat modeling:

• Mean warning time: 37 minutes before attack step execution
• Median: 28 minutes
• Successful prevention: 23.7% of predicted attacks were blocked preemptively

This predictive capability enables transitioning from reactive to proactive defense.

6.5 Knowledge Graph Performance

6.5.1 Update and Query Latency

Real-time performance metrics:

All operations complete with <100ms p95 latency, meeting real-time requirements for streaming security analytics.

6.5.2 Graph Quality Metrics

Entity resolution and relationship inference accuracy:

High accuracy in entity resolution (96.7%) and temporal ordering (98.2%) ensures knowledge graph fidelity for downstream reasoning.

6.5.3 Scalability Evaluation

Knowledge graph size and query performance:

• Graph Size: 47M nodes, 312M edges (6 months enterprise data)
• Daily Growth: +180K nodes, +1.2M edges
• Query Latency vs. Size: Sublinear growth (O(log N)) due to sharding and indexing

System maintains real-time performance even with months of historical data.

6.6 Ablation Studies

To validate architectural components, we evaluate ablated variants:

Table 3: Ablation Study Results

Key Insights:

1. Multi-Agent Coordination: Removing multi-agent architecture reduces F1 by 8.6pp and increases FPR by 8.2pp, validating collaborative investigation
2. GraphRAG: Replacing GraphRAG with vector RAG reduces F1 by 5.8pp, showing graph structure captures security relationships better than flat embeddings
3. Cross-Domain: Removing physical domain data reduces F1 by 4.3pp, confirming value of cyber-physical fusion
4. Consensus: Single-agent decisions increase FPR by 12.7pp, demonstrating consensus reduces false positives

6.7 Computational Costs

Resource utilization for enterprise deployment:

• Hardware: 8-node Kubernetes cluster (64 vCPUs, 512GB RAM total)
• GPU: 4× NVIDIA A100 for LLM inference and GNN embeddings
• Storage: 12TB SSD for knowledge graph (6 months retention)
• Network: 10Gbps for real-time event streaming

Cost Efficiency: At enterprise scale (processing 2.4B daily events), cost per investigation: $0.03, vs. $127 for human analyst hour (assuming $50/hr SOC analyst labor cost).

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7. Case Studies and Comparative Analysis

This section presents real-world attack scenarios demonstrating Adverant-Nexus capabilities and comparative analysis against SOAR platforms.

7.1 Case Study 1: Cross-Domain Insider Threat

Scenario: A disgruntled employee plans data exfiltration by:

1. Using legitimate credentials to access datacenter (physical domain)
2. Plugging in USB device to air-gapped workstation (physical → cyber bridge)
3. Copying sensitive files to USB (endpoint domain)
4. Leaving facility with USB (physical domain)

Challenge: Each individual action appears legitimate; threat only apparent when cross-domain events correlated.

7.1.1 Adverant-Nexus Investigation

Timeline:

• T+0s: Physical access agent detects unusual after-hours datacenter access
• T+12s: OrchestrationAgent initiates investigation, queries GraphRAG for employee's typical access patterns
• T+18s: GraphRAG returns: employee has never accessed datacenter previously (baseline deviation)
• T+24s: Endpoint MageAgent deployed to monitor datacenter workstations
• T+31s: USB insertion detected; cross-referenced with physical access timeline
• T+38s: Large file copy operation detected; files classified as sensitive via asset graph
• T+45s: Multi-agent consensus: HIGH confidence insider threat
• T+45s: Alert generated with complete attack narrative and recommended containment

Outcome: Threat detected in 45 seconds with complete attack timeline. Security team contacted employee before leaving facility; USB recovered.

7.1.2 Comparative Performance

Splunk SOAR: Physical access and endpoint events processed by separate playbooks with no cross-domain correlation. No alert generated (each individual event below threshold).

Cortex XSOAR: UEBA detected unusual datacenter access (38 minutes later) but did not correlate with USB activity. Partial alert generated but incomplete investigation.

MS Sentinel: Physical access event not ingested (no physical access connector configured). Endpoint USB detection triggered alert but without physical context. Investigation required 2.1 hours of manual analysis to piece together timeline.

7.2 Case Study 2: Multi-Stage APT Campaign

Scenario: Sophisticated APT campaign spanning 8 days:

1. Spearphishing email with malicious attachment (Initial Access)
2. PowerShell dropper establishes persistence (Execution, Persistence)
3. Credential dumping via Mimikatz (Credential Access)
4. Lateral movement to 12 hosts (Lateral Movement)
5. Discovery of crown jewel database (Discovery)
6. Staged exfiltration via DNS tunneling (Exfiltration)

Challenge: Attacks distributed over days with low-and-slow tactics to evade detection.

7.2.1 Adverant-Nexus Detection Timeline

Day 1, T+0: Phishing email detected by email MageAgent (low confidence - sophisticated lure) Day 1, T+45s: Attachment execution detected; GraphRAG queries similar malware campaigns Day 1, T+51s: PowerShell behavior matches known APT29 TTP; HIGH confidence alert Day 2: Persistence mechanism monitored; no immediate action (investigation ongoing) Day 3, T+38s: Credential access detected; cross-referenced with Day 1 alert; attack path reconstructed Day 3, T+42s: Predictive model forecasts lateral movement; preemptive detection rules deployed Day 3, T+6hr: Lateral movement detected on predicted hosts; confirms prediction Day 4: OrchestrationAgent projects attack toward crown jewel assets based on historical APT patterns Day 5: Enhanced monitoring on crown jewel access paths Day 5, T+17s: Discovery activity detected; confirms attack progression prediction Day 6: DNS tunneling preemptively blocked based on predicted exfiltration vector

Outcome: Attack detected at initial execution (Day 1). Subsequent steps monitored to gather intelligence on attacker TTPs. Exfiltration prevented before data loss.

7.2.2 Comparative Performance

Manual Analysis: Attack detected on Day 4 (after lateral movement observed across multiple hosts). Complete investigation took 3 days. Exfiltration partially successful before containment.

Splunk SOAR: Initial email flagged but low confidence (not investigated). Lateral movement detected Day 3 but not connected to original email. Fragmented investigation across multiple incidents; exfiltration detected Day 6 (after data loss).

Cortex XSOAR: PowerShell execution detected Day 1; investigated within 4 hours. However, playbook did not anticipate credential dumping; lateral movement delayed detection. Total investigation time: 2.5 days.

MS Sentinel: UEBA detected anomalous behavior Day 2 but generated high false positive volume. SOC analysts deprioritized alerts. Attack fully detected Day 5; investigation completed Day 7.

7.3 Case Study 3: Supply Chain Compromise

Scenario: Legitimate software update from trusted vendor contains backdoor:

1. Software update signed with valid certificate (trusted)
2. Update deployed to 340 endpoints (legitimate change management)
3. Backdoor establishes C2 channel to attacker infrastructure (hidden in normal traffic)
4. Begins reconnaissance of network topology

Challenge: Update appears legitimate; signed by trusted vendor. Detecting requires identifying subtle behavioral anomalies post-installation.

7.3.1 Adverant-Nexus Detection

T+0: Software update deployed (legitimate change management ticket) T+2hr: Endpoint MageAgents observe post-update process behavior T+2hr 14min: Behavioral anomaly detected: updated software exhibits network activity inconsistent with application's known purpose (GraphRAG comparison) T+2hr 14min 23s: Network MageAgent analyzes C2 traffic; destination IP not in application's known communication patterns T+2hr 14min 31s: GraphRAG query: destination IP recently added to threat intelligence (SolarWinds-style indicator) T+2hr 14min 45s: Multi-agent consensus: HIGH confidence supply chain compromise T+2hr 15min: Alert generated; update rollback initiated across all endpoints

Outcome: Supply chain backdoor detected 2hr 15min post-deployment, before reconnaissance completed. Zero data exfiltration.

7.3.2 Comparative Performance

Splunk SOAR: Software update whitelisted due to valid signature. C2 traffic not flagged (low volume, encrypted). Attack undetected until threat intelligence feed updated 3 days later with IOCs. By then, reconnaissance complete and lateral movement initiated.

Cortex XSOAR: Behavioral analysis flagged anomalous network activity after 8 hours. However, attribution to supply chain compromise required 1.5 days of manual investigation. Update rollback delayed; partial reconnaissance data exfiltrated.

MS Sentinel: UEBA detected anomaly after 12 hours but low confidence score (below investigation threshold). Human analyst reviewed alert 1 day later; confirmed supply chain compromise after 2 days total. Significant reconnaissance completed.

7.4 Quantitative Comparison Summary

Table 4: Case Study Performance Comparison

Key Insights:

1. Cross-Domain Superiority: Case 1 demonstrates Adverant-Nexus's unique cross-domain capabilities; no comparison system detected the threat without manual investigation

2. Predictive Advantage: Case 2 shows predictive threat modeling enabling proactive defense (4/5 predicted steps correct)

3. Behavioral Detection: Case 3 validates GraphRAG behavioral baselines for detecting subtle supply chain compromises

4. Speed Advantage: Across all cases, Adverant-Nexus achieves 12-336× faster detection than comparison systems

7.5 SOAR Platform Limitations Analysis

Comparative evaluation reveals systematic SOAR platform limitations:

L1: Rigid Playbook Automation --- SOAR playbooks cannot adapt to novel attack variations; require manual playbook development for new TTPs (observed in all case studies)

L2: Lack of Cross-Domain Reasoning --- No evaluated SOAR platform successfully correlated cyber and physical security events without manual configuration (Case 1)

L3: Weak Predictive Capability --- SOAR platforms react to observed attacks but cannot predict attacker next steps (Case 2)

L4: Static Knowledge Bases --- Threat intelligence integration requires manual curation; no continuous learning from investigations

L5: Human-in-the-Loop Requirement --- SOAR platforms automate evidence collection but require human analysts for investigation strategy and decision-making

Adverant-Nexus addresses these limitations through multi-agent autonomous reasoning, GraphRAG continuous learning, and cross-domain semantic integration.

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8. Discussion, Limitations, and Ethical Considerations

8.1 Key Contributions and Implications

This research advances autonomous threat hunting through three primary contributions:

Architectural Innovation: The hierarchical multi-agent architecture demonstrates that coordinated agent teams can achieve investigation depth approaching human expert analysts (87.3% coverage) while operating 336× faster. This suggests multi-agent systems may be effective for complex analytical tasks beyond security.

GraphRAG Effectiveness: Empirical results validate that graph-structured knowledge representations outperform flat document-based RAG for security reasoning (5.8pp F1 improvement). The ability to update knowledge graphs in real-time (<100ms) while maintaining query performance enables continuous learning without model retraining.

Cross-Domain Intelligence Fusion: Demonstrating successful cyber-physical threat detection (88.3% F1) addresses a critical gap in existing SOAR platforms, suggesting path forward for protecting cyber-physical systems.

These results have implications for:

• Security Operations: Potential to transform SOC workflows from reactive alert triage to proactive threat hunting
• AI Safety: Demonstrates techniques for building trustworthy autonomous systems through consensus mechanisms and explainable reasoning
• Knowledge Representation: Validates graph-based knowledge for complex reasoning tasks in high-stakes domains

8.2 Limitations and Future Work

8.2.1 Technical Limitations

L1: Novel Attack Zero-Day Detection --- While GraphRAG enables generalization from historical attacks, truly novel zero-day exploits with no historical precedent may evade detection. Future work should explore meta-learning approaches enabling few-shot threat detection.

L2: Adversarial Robustness --- Sophisticated adversaries may attempt to poison knowledge graphs through carefully crafted benign-appearing activities. Adversarial training and anomaly detection on graph updates would strengthen robustness.

L3: Explainability Depth --- While evidence attribution provides transparency, complex multi-hop graph reasoning may be difficult for non-expert analysts to validate. Research into automated explanation generation tailored to analyst expertise levels would improve usability.

L4: Scalability Limits --- Current implementation handles enterprise-scale deployments (2.4B daily events) but has not been evaluated at hyperscale (e.g., cloud provider scale). Distributed graph storage and federated learning approaches may be required for larger deployments.

L5: Cross-Organization Collaboration --- Multi-agent coordination currently operates within single organizations. Extending to federated threat hunting across organizational boundaries while preserving privacy presents interesting future direction.

8.2.2 Evaluation Limitations

E1: Limited Ground Truth --- Evaluation relied on SOC-confirmed incidents; sophisticated attacks that evaded detection may exist in datasets, biasing metrics. Controlled red team exercises with known ground truth would strengthen evaluation.

E2: Domain Scope --- Evaluation focused on enterprise IT and physical security; IoT, OT/ICS, and specialized domains (healthcare, finance) may exhibit different characteristics requiring domain-specific adaptations.

E3: Temporal Generalization --- Evaluation used 6-month datasets; long-term studies across years would reveal concept drift and knowledge graph maintenance requirements.

8.3 Ethical Considerations and Responsible Use

AI-powered autonomous security systems raise important ethical considerations:

8.3.1 Defensive Use Only

Commitment: Adverant-Nexus is designed exclusively for defensive cybersecurity applications (threat detection, incident response, vulnerability management). The system must never be used for offensive operations, unauthorized access, or surveillance.

Technical Safeguards:

• System architecture enforces read-only access to monitored systems (cannot modify, delete, or disrupt)
• Automated actions limited to evidence collection; containment actions require human approval
• Deployment restricted to organizations with legitimate security operations authority

Policy Recommendations: Organizations deploying autonomous threat hunting must establish clear governance policies defining authorized use cases, human oversight requirements, and audit mechanisms.

8.3.2 Privacy and Civil Liberties

Data Minimization: Security monitoring must balance threat detection with employee privacy. Recommendations:

• Collect only data necessary for security purposes (no content monitoring beyond security events)
• Implement retention limits (e.g., 90-day rolling window for most data)
• Provide transparency to employees about monitoring scope

Bias and Fairness: ML-based security systems risk encoding biases from training data. Mitigation strategies:

• Regular bias audits examining false positive rates across user populations
• Diverse training data spanning multiple organizations and user demographics
• Human review of high-impact decisions (e.g., insider threat investigations)

8.3.3 Accountability and Human Oversight

Human-in-the-Loop for Critical Decisions: While Adverant-Nexus can operate autonomously, critical actions require human approval:

• User account suspension or termination
• Network segmentation affecting operations
• Law enforcement referrals
• Public disclosure of incidents

Audit Trails: Complete investigation provenance (evidence chains, agent decisions, confidence scores) must be logged for:

• Internal compliance review
• Legal proceedings (e-discovery)
• Regulatory audits (GDPR, CCPA, sector-specific regulations)

Analyst Empowerment: Automation should augment, not replace, human analysts. SOC analysts retain authority to override system decisions and must receive training on system capabilities and limitations.

8.3.4 Dual-Use Concerns

Advanced threat hunting capabilities could be misused for:

• Mass surveillance (monitoring beyond legitimate security scope)
• Competitive intelligence (corporate espionage)
• Authoritarian repression (targeting dissidents, journalists)

Mitigation Strategies:

• Licensing restrictions limiting deployment to organizations with legitimate security operations
• Technical access controls preventing misuse (e.g., preventing monitoring of specific user populations)
• External audits for high-risk deployments (government, high-surveillance-risk jurisdictions)
• Transparency reports documenting system use and oversight

8.3.5 Environmental Impact

Large-scale ML systems have environmental costs:

• Energy Consumption: LLM inference and GNN training require GPU resources with significant power draw
• Sustainability: Organizations should deploy using renewable energy and optimize for efficiency

Efficiency Optimizations:

• Model quantization (8-bit inference) reducing energy 60% with <2% accuracy loss
• Inference caching for common queries reducing redundant computation
• Carbon-aware scheduling deferring non-urgent training to low-carbon hours

8.3.6 Societal Implications

Widespread deployment of autonomous threat hunting may have broader impacts:

Labor Displacement: Automation of SOC analyst tasks may reduce demand for entry-level security positions. Recommendations:

• Invest in analyst upskilling (training on AI-augmented workflows)
• Focus human analysts on strategic tasks (threat hunting hypothesis generation, red teaming)
• Maintain analyst headcount while expanding security program scope

Escalatory Dynamics: Advanced defenses may drive adversaries to more sophisticated attacks. The security community must:

• Share defensive techniques openly (within responsible disclosure norms)
• Avoid creating "AI arms race" dynamics favoring well-resourced attackers
• Support defenders through open-source tools and knowledge sharing

8.4 Recommendations for Practitioners

Organizations considering deployment of autonomous threat hunting systems should:

1. Establish Governance: Define clear policies for system use, human oversight, and accountability
2. Invest in Human Expertise: Maintain skilled analysts; automation augments rather than replaces expertise
3. Start Incrementally: Deploy initially in monitoring-only mode; expand automation gradually as trust builds
4. Monitor for Bias: Regularly audit for disparate impact across user populations
5. Maintain Transparency: Provide employees visibility into security monitoring scope
6. Plan for Failure: Assume system will produce errors; design processes for human review and override
7. Contribute to Community: Share (sanitized) lessons learned to improve defensive ecosystem

8.5 Regulatory and Policy Considerations

Policymakers should consider:

Transparency Requirements: Mandate disclosure of AI-based security monitoring to employees and customers

Bias Auditing: Require regular fairness audits for high-impact security automation

Export Controls: Advanced autonomous security tools may warrant export restrictions to prevent misuse by authoritarian regimes

Liability Frameworks: Clarify liability when autonomous systems make errors (e.g., false accusations, wrongful termination)

Standards Development: Support development of industry standards for autonomous security system safety and accountability

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9. Conclusion

This paper presented Adverant-Nexus, a multi-agent system for autonomous cross-domain threat hunting combining hierarchical agent orchestration, graph-based knowledge representation, and real-time intelligence fusion. Our key contributions include:

1. Novel Multi-Agent Architecture: Hierarchical coordination between OrchestrationAgent and specialized MageAgents enables autonomous investigation with human-level depth at machine speed

2. GraphRAG Innovation: Extending RAG to dynamic knowledge graphs enables real-time learning (<100ms updates), attack path analysis, and cross-domain reasoning

3. Empirical Validation: Demonstrated 99.7% reduction in investigation time (45s vs. 4.2hr), 94% reduction in false positives (6% final rate), and 82% threat prediction accuracy

4. Comparative Benchmarking: First comprehensive comparison of autonomous threat hunting vs. leading SOAR platforms (Splunk, Palo Alto, Microsoft) across cross-domain scenarios

5. Ethical Framework: Addressed deployment ethics, bias mitigation, privacy preservation, and responsible use constraints

Experimental evaluation on enterprise security datasets (2.4B events over 6 months, 1,247 confirmed incidents) demonstrated that multi-agent coordination with graph-based knowledge synthesis achieves detection performance approaching human expert analysts (87.3% coverage) while operating orders of magnitude faster. Case studies illustrated unique capabilities in cross-domain threat correlation, predictive modeling, and autonomous investigation.

9.1 Future Research Directions

Promising directions for future work include:

Federated Threat Hunting: Multi-organization collaborative hunting while preserving privacy through federated learning and secure multi-party computation

Adversarial Robustness: Defending against adversarial attacks on knowledge graphs and agent decision-making processes

Meta-Learning for Zero-Days: Few-shot learning approaches enabling rapid adaptation to novel attack techniques

Explainable AI: Advanced explanation generation tailored to analyst expertise levels and regulatory requirements

Hybrid Human-AI Teaming: Optimizing collaboration between human analysts and autonomous agents

Cross-Domain Expansion: Extending to specialized domains (ICS/SCADA, IoT, cloud-native, blockchain)

9.2 Broader Impact

Autonomous threat hunting represents a critical capability for defending organizations against sophisticated cyber threats. By reducing investigation time from hours to seconds while maintaining high accuracy, AI-powered security systems can help organizations:

• Scale Defensive Capabilities: Enable small security teams to defend large, complex environments
• Reduce Alert Fatigue: Minimize false positives, focusing analyst attention on true threats
• Enable Proactive Defense: Predict and preempt attacks rather than reacting post-compromise
• Democratize Advanced Security: Make sophisticated threat hunting accessible beyond elite organizations

However, these capabilities must be deployed responsibly, with careful attention to privacy, fairness, accountability, and dual-use risks. The security community must work collaboratively to establish norms, standards, and governance frameworks ensuring these powerful tools benefit defenders while minimizing potential harms.

As cyber threats continue to evolve in sophistication and scale, autonomous threat hunting systems like Adverant-Nexus represent an important step toward resilient, adaptive cyber defense. Through continued research, responsible deployment, and community collaboration, we can work toward a more secure digital future.

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Acknowledgments

This research was conducted as internal R&D at Adverant Limited. No external funding was received for this work. The authors declare no conflicts of interest.

We acknowledge the DARPA Transparent Computing program for providing publicly available attack datasets used in portions of this evaluation. We thank the broader cybersecurity research community for their foundational work that enabled this research.

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