AgenticCyber: AI-Driven Cyber Defense

Updated 13 December 2025

AgenticCyber is a family of AI-driven cybersecurity frameworks featuring autonomous agents, adaptive learning, and ethical governance to address emerging threats.
It utilizes a layered architecture that integrates autonomous decision-making, explicit ethical constraints, and human oversight for dynamic threat detection and policy enforcement.
The framework leverages formal methodologies such as MDPs, reinforcement learning, and game theory to optimize security protocols and ensure compliance with international standards.

AgenticCyber denotes the family of AI-powered cybersecurity frameworks and operational paradigms that employ autonomous, goal-driven agents (typically structured as multi-layered, multi-agent systems) to deliver adaptive threat detection, situational awareness, dynamic policy enforcement, and ethically governed, auditable defense workflows across digital infrastructures. AgenticCyber systems are characterized by their capacity for real-time learning, automated planning and reasoning, decentralized decision-making, rigorous formalization of safety and functional properties, and integration of human oversight and ethical constraints, distinguishing them from traditional static or rule-based security architectures (Adabara et al., 8 Dec 2025, Vinay, 7 Dec 2025, Oesch et al., 10 Feb 2025, Tallam, 28 Feb 2025, Olayinka et al., 25 Sep 2025, Allegrini et al., 15 Oct 2025, Roy, 6 Dec 2025, Zambare et al., 12 Aug 2025, Challita et al., 11 May 2025, Zhu, 14 Jul 2025, Blefari et al., 3 Jul 2025, Christodorescu et al., 1 Dec 2025, Dif et al., 8 Sep 2025).

1. Architectural Principles and Agentic AI Layers

AgenticCyber frameworks universally adopt modular, layered architectures, enabling separation of concerns among autonomous decision-making, policy-driven governance, and human oversight. A canonical instance is a three-layer design:

Autonomous Decision Layer: Implements agents (e.g., tabular Q-learning or neural RL-based) responsible for real-time sensing, feature extraction, decision proposal (e.g., allow/block network flows) (Adabara et al., 8 Dec 2025, Zambare et al., 12 Aug 2025).
Ethical Governance Layer: Encodes hard operational constraints (e.g., false-positive ceilings, fairness regularizers), modifying agent rewards or vetoing unsafe decisions by filtering proposals through explicit policy functions (Adabara et al., 8 Dec 2025, Tallam, 28 Feb 2025).
Human Oversight Layer: Maintains audit logs, analyst dashboards for real-time review or override, and provides post-hoc compliance artifacts for formal audits (Adabara et al., 8 Dec 2025, Tallam, 28 Feb 2025).

More advanced instantiations introduce additional layers for perception, multimodal analysis, orchestration, and adaptive response (e.g., multimodal agents for log, video, and audio analysis orchestrated via high-level GenAI/LangChain workflows) (Roy, 6 Dec 2025).

This structurally enforces agent autonomy at the edge, centralized semantic coordination, and robust compliance interfaces, which is essential for regulatory compatibility (NIST SP 800-207 Zero-Trust, ISO/IEC 27001, GDPR/CCPA) (Olayinka et al., 25 Sep 2025).

2. Formalization of Reasoning, Learning, and Safety

AgenticCyber systems re-contextualize cybersecurity workflows using Markov Decision Processes (MDPs), reinforcement learning, dynamic multi-agent planning, and formal protocol modeling:

MDP/RL Foundation: States $S$ encode traffic types or behavioral fingerprints; actions $A$ represent security policy choices (quarantine, throttle, update); reward functions $R$ balance detection, mitigation, and penalize false positives (Adabara et al., 8 Dec 2025, Olayinka et al., 25 Sep 2025).
Reward Augmentation: Incorporation of ethical penalty terms and fairness constraints, e.g.,

$\text{Penalty} = -\lambda \max(0, FPR_\epsilon - \tau)$

with $R_e(s,a) = R(s,a) + \text{Penalty}$ discouraging excessive false positives (Adabara et al., 8 Dec 2025).

Dynamic Multi-Agent Workflows: Dependency DAGs for task decomposition, formal host-agent and task-lifecycle models specifying state transitions, orchestration, fallback, retry, and error states (CTL/LTL properties for verification) (Allegrini et al., 15 Oct 2025).
Game-Theoretic Integration: Embedding Stackelberg, Nash, Bayesian, and signaling games within LLM-agent pipelines to model adversarial interactions, trust updating, and equilibrium-optimized policy synthesis (Zhu, 14 Jul 2025, Oesch et al., 10 Feb 2025).
Memory and Reasoning: External memory buffers, recursive plan correction, context window handling (token-compressed summarization), adaptive multi-modal fusion with attention-based reasoning (Challita et al., 11 May 2025, Roy, 6 Dec 2025).

Formal safety properties (host-agent and task-lifecycle) underpin assurance of liveness, safety, completeness, and fairness, enabling model checking for deadlock-freedom and privilege containment (Allegrini et al., 15 Oct 2025, Christodorescu et al., 1 Dec 2025).

3. Multimodal, Distributed, and Edge-Centric AgenticCyber

Distributed agentic pipelines span cloud-native, edge, mobile, and IoT layers:

Node-level Micro-Agents: Autonomous RL or LLM-based modules perform feature extraction, local anomaly detection, thresholding, and context-aware classification; decisions and telemetry summaries are aggregated by central controllers for cross-node situational awareness (Zambare et al., 12 Aug 2025, Olayinka et al., 25 Sep 2025, Roy, 6 Dec 2025).
Multimodal Perception and Fusion: Specialized agents ingest log events (Isolation Forests), video streams (autoencoder reconstruction error, vision LLM), audio data (YAMNet, GMM likelihoods), and fuse threat scores via scaled dot-product attention (Gemini, LangChain), maximizing cross-modal interpretability and detection reliability (Roy, 6 Dec 2025).
Federated Intelligence and Learning: TAXII/STIX protocols for IoC sharing, gradient/model exchange (gRPC, differential privacy), decentralized trust-weighted risk aggregation (Olayinka et al., 25 Sep 2025).
Scalability and Resource-Awareness: Designs optimize for low CPU/memory impact, asynchronous event-driven processing, idle resource minimization, and adaptive sampling (Adabara et al., 8 Dec 2025, Zambare et al., 12 Aug 2025).

4. Ethical Governance, Auditability, and Compliance

AgenticCyber systems actively enforce fairness, transparency, explainability, and ethical oversight:

Explicit Governance Functions: Policy constraints on agent actions, e.g., $\big|\mathbb{E}[\pi(a|s)|d=0] - \mathbb{E}[\pi(a|s)|d=1]\big| \leq \epsilon$ for fairness across protected dimensions (Tallam, 28 Feb 2025).
Audit Trails and Explainability: Provenance vectors $\mathbf{e}_t = \{f_i(x_t), w_i, \frac{\partial T}{\partial f_i}\}$ , append-only logs, dashboard interfaces for forensic drill-down and dynamic incident intervention (Adabara et al., 8 Dec 2025, Tallam, 28 Feb 2025).
Human-in-the-Loop Overrides: Layered approval gates for high-impact actions, rollback facilities, and dynamic feedback incorporation to improve agent learning (Adabara et al., 8 Dec 2025, Roy, 6 Dec 2025).
Policy Compliance and Standard Adherence: Alignment with international standards (ISO/IEC 27001, NIST CSF), transparency in mitigation logic, and explainability scoring (BARTScore, GPT-4 judge) (Olayinka et al., 25 Sep 2025, Blefari et al., 3 Jul 2025, Dif et al., 8 Sep 2025).

5. Offense, Defense, and Co-Evolutionary Dynamics

AgenticCyber encompasses both offensive (red-teaming, autonomous pentesting) and defensive (containment, recovery, continuous protection) workflows:

Offensive Capabilities: Autonomous exploit discovery and refinement (LLM-guided fuzzing, plan correction, memory management), multi-step reasoning loops, context window constraint handling (Challita et al., 11 May 2025, Oesch et al., 10 Feb 2025).
Defense Automation: Log anomaly detection (autoencoders, probabilistic models), risk scoring, fast response (mean latency down to 220 ms), zero-day identification (dynamic behavioral baselining), active mitigation (quarantine, throttling) (Olayinka et al., 25 Sep 2025, Adabara et al., 8 Dec 2025, Zambare et al., 12 Aug 2025).
Red/Blue Co-Evolution: Stochastic game-theoretic frameworks, co-trained adversarial agents (PPO updates), empirical demonstrations of arms-race dynamics in SOC testbeds (Oesch et al., 10 Feb 2025).

Empirical results indicate agentic architectures outperform static signature-based models across accuracy, response time, and adaptability metrics (e.g., F1-score 96.2%, detection rate improvement up to 28 pp for zero-day attacks, MTTR reduction by 65%) (Roy, 6 Dec 2025, Olayinka et al., 25 Sep 2025, Adabara et al., 8 Dec 2025, Tallam, 28 Feb 2025).

6. Security Foundations, Threat Models, and Verification

Securing agentic cyber systems requires rigorous application of classical system security principles under new adversary models:

Defensive Invariants: Least privilege, complete mediation, TCB tamper resistance, secure information flow, and secure-by-default operation across agent and tool invocation boundaries (Christodorescu et al., 1 Dec 2025).
Adversary Models: Black-box agent access, prompt/tool injection, privilege escalation, coordinated multi-step exfiltration, bounded by OS and cryptographic isolation (Christodorescu et al., 1 Dec 2025).
Case Studies of Real Attacks: Extensive documentation of breaches exploiting weak policy enforcement, incomplete mediation, UI social engineering, and information flow leaks; corresponding recommendations for multi-layered guardrails, policy DSLs, and continuous improvement cycles (Christodorescu et al., 1 Dec 2025).
Formal Verification: Model checking (NuSMV, SPIN) of host-agent and task-lifecycle properties, protocol uniformity, validation modules for safe invocation, instrumentation for runtime deviation detection (Allegrini et al., 15 Oct 2025, Christodorescu et al., 1 Dec 2025).

7. Benchmarking, Extensibility, and Future Research Directions

AgenticCyber systems are evaluated with specialized SOC and multi-agent benchmarks:

Benchmarks: AgentBench (multi-stage planning), DefenderBench (adversarial scenarios), CyberSOCEval (malware reasoning), SecEval/CyBench (CTF and ATT&CK mapping), RedTeamLLM (pentesting CTFs), CyberRAG (specialist classifier orchestration) (Vinay, 7 Dec 2025, Blefari et al., 3 Jul 2025, Challita et al., 11 May 2025).
Extensibility: Modular registration mechanisms enable addition of new attack classifiers and retrieval knowledge bases without core agent retraining; semantic orchestration supports plug-and-play expansion (Blefari et al., 3 Jul 2025).
Open Research Challenges: Provable security for probabilistic TCBs, instruction–data separation, dynamic NL policy inference, tractable LLM information-flow control, security-aware neural architectures, robust multi-agent coordination and alignment metrics (Christodorescu et al., 1 Dec 2025, Allegrini et al., 15 Oct 2025, Vinay, 7 Dec 2025).

AgenticCyber, encompassing architectures, formalized workflows, ethical governance, and security hardening, provides the technical foundation for scalable, auditable, and trustworthy AI-driven cyber defense across diverse organizational and national infrastructures.