Agentic AI Forensics & Incident Response (A-AFIR) Fundamentals Training by Tonex
Modern incident teams increasingly face autonomous and semi-autonomous AI agents that can reason, plan, and act across tools and APIs. This course equips professionals to investigate those agents with rigor: what they stored, which tools they called, and how they coordinated in swarms. The impact on cybersecurity is immediate—faster detection of compromised agent policies, safer containment of misaligned actions, and stronger assurance around AI-driven workflows. Participants learn to turn opaque agent behavior into defensible evidence while improving enterprise readiness for AI-enabled threats and failures.
Learning Objectives
- Trace, preserve, and analyze agent memory artifacts across vector stores and caches
- Reconstruct tool-call histories and correlate them with external systems of record
- Map behaviors inside multi-agent swarms and identify emergent risks
- Execute safe containment and rollback for rogue or poisoned agents
- Design response playbooks tailored to agentic systems; strengthen governance and auditability
- Elevate cybersecurity posture by integrating A-AFIR findings into SOC workflows and risk reporting
Audience
- Cybersecurity Professionals
- DFIR and threat hunting teams
- SOC managers and incident commanders
- AI/ML engineers and MLOps practitioners
- Risk, audit, and compliance officers
- Product security and platform reliability leaders
Course Modules
Module 1 – Agent Memory Forensics
- Identify volatile vs persistent agent memory layers
- Extract prompt, plan, and scratchpad residues
- Parse embeddings, keys, and retrieval traces
- Correlate memory to actions and outputs
- Validate provenance and chain-of-custody
- Build timelines from memory diffs
Module 2 – Tool-Call Forensics
- Enumerate tools, functions, and API surfaces
- Collect arguments, outputs, and side effects
- Link calls to identity, policy, and scope
- Verify idempotency and replay consistency
- Detect prompt injection via call patterns
- Attribute impact across downstream systems
Module 3 – Multi-Agent Swarm Investigation
- Diagram roles, goals, and communication paths
- Capture coordinator/worker message flows
- Quantify consensus, voting, or critique steps
- Surface emergent behaviors and cascades
- Isolate compromised or noisy agents quickly
- Prioritize evidence for swarm root cause
Module 4 – Rogue-Agent Containment
- Define guardrails, tripwires, and safeties
- Quarantine agents with reversible controls
- Revoke capabilities, tokens, and sessions
- Snapshot state for later reproducibility
- Orchestrate rollback and clean re-enable
- Document evidence for post-mortem review
Module 5 – Autonomy Threat Modeling
- Profile attack surfaces unique to agents
- Model data, prompt, and tool abuse paths
- Rank risks with impact/likelihood scoring
- Map controls to NIST/ISO/AI governance
- Design detective/preventive guardrails
- Validate scenarios with red-blue exercises
Module 6 – Governance & Playbooks
- Establish A-AFIR roles and escalation
- Standardize evidence formats and fields
- Integrate with SIEM/SOAR and ticketing
- Define KPIs, SLAs, and decision gates
- Align with legal, privacy, and ethics
- Iterate playbooks from lessons learned
Strengthen your team’s readiness for agentic AI incidents. Enroll in A-AFIR Fundamentals by Tonex to master investigation, attribution, and safe containment of autonomous agents—so you can detect, prove, and stop unsafe behavior with confidence.