AI Agents & Autonomous Systems Security Certification (AAASSC) Certification Program by Tonex
AI Agents & Autonomous Systems Security Certification (AAASSC) Certification Program by Tonex equips professionals to secure agentic and autonomous AI at scale. The program covers multi-agent systems, autonomous behavior, and swarm coordination. You learn how agents perceive, plan, collaborate, and act under constraints. You also learn how adversaries manipulate goals, memory, world models, and communications. Emphasis is on security-by-design, verifiable behavior, and resilient operations. The curriculum maps threats to defenses across the full lifecycle.
Topics include identity, attestation, runtime monitoring, safety cases, and incident response. The impact on cybersecurity is significant. Autonomous agents expand the attack surface, accelerate kill chains, and amplify cascade risk across fleets. Strong governance, trustworthy deployment, and recovery playbooks become essential. Graduates leave ready to assess risk, harden architectures, and lead secure rollouts of agent-based solutions.
Learning Objectives:
- Explain agent architectures, autonomy levels, and coordination patterns
- Model threats against agents, swarms, and tool-using LLM agents
- Design guardrails, constraints, and policy enforcement for safe actions
- Implement identity, attestation, and zero-trust controls for agents
- Detect manipulation with runtime monitoring and anomaly signals
- Plan containment, rollback, and recovery for compromised agents
- Build safety cases and assurance evidence for regulated contexts
- Govern deployments with risk, compliance, and change controls
Audience:
- Cybersecurity professionals
- Security architects and engineers
- AI/ML engineers and platform owners
- Autonomous systems and controls engineers
- DevSecOps and SRE leaders
- Risk, compliance, and product managers
Program Modules:
Module 1: Foundations of Autonomous Agents & MAS
- Agent architectures and autonomy levels
- Perception, memory, and world models
- Planning, policy, and tool use
- Coordination and consensus basics
- Safety constraints and guardrails
- Threat landscape overview
Module 2: Adversarial Threats to Agents
- Prompt/goal injection and policy evasion
- Reward hacking and specification gaming
- Adversarial perception and model poisoning
- Sybil, collusion, and Byzantine behaviors
- Memory, state, and world-model tampering
- Takeover paths and kill-chain mapping
Module 3: Defensive Design for Autonomous Behavior
- Verification and validation strategies
- Runtime monitors and anomaly detection
- Action filters, shields, and fallback modes
- Secrets handling and least-privilege tools
- Human-on-the-loop and escalation gates
- Tabletop red-team exercises and reviews
Module 4: Securing Swarms & Multi-Agent Communications
- Identity, provenance, and attestation
- Key management and trust establishment
- Resilient comms and fault tolerance
- Reputation, trust scores, and misbehavior flags
- Data governance, logging, and telemetry
- BFT patterns and consensus hardening
Module 5: Resilience, Safety, and Recovery
- Containment and circuit-breaker design
- Quarantine, rollback, and state reset
- Graceful degradation and safe stop
- IR playbooks for autonomous incidents
- Forensics and audit trails for agents
- Continuous assurance and readiness tests
Module 6: Governance, Compliance, and Lifecycle
- Risk assessment for autonomy programs
- Model supply chain and signing policies
- Change management and deployment gates
- Safety cases and assurance artifacts
- Ethics, RoE, and operational boundaries
- Metrics, KPIs, and executive reporting
Exam Domains:
- Adversarial Threat Modeling for Autonomous Agents
- Secure Coordination and Agent Governance
- Swarm Resilience and Collective Defense
- Assurance, Verification, and Safety Cases
- Model Integrity and AI Supply Chain Security
- Regulatory, Ethical, and Operational Risk Management
Course Delivery:
The course is delivered through lectures, interactive discussions, guided demonstrations, and case studies, facilitated by experts in AAASSC. Participants gain access to online resources, including readings, templates, and curated examples for structured take-home exercises. No simulations, no labs, no machines.
Assessment and Certification:
Participants are assessed through quizzes, short assignments, and a capstone case analysis. Upon successful completion, participants receive a certificate in AI Agents & Autonomous Systems Security Certification (AAASSC).
Question Types:
- Multiple Choice Questions (MCQs)
- Scenario-based Questions
Passing Criteria:
To pass the AI Agents & Autonomous Systems Security Certification (AAASSC) Certification Training exam, candidates must achieve a score of 70% or higher.
Ready to secure autonomous agents with confidence? Enroll in AAASSC by Tonex and lead safer deployments. Bring your team, align on best practices, and build trust in autonomy.