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Course Outline
Foundations: Threat Modeling for Autonomous AI Systems
- Classification of autonomous threats, including misuse, privilege escalation, data exfiltration, and supply-chain vulnerabilities
- Profiling adversaries and assessing attacker capabilities specific to autonomous agent operations
- Identifying critical assets, establishing trust boundaries, and determining essential control points for agent interactions
Governance, Policy, and Risk Management
- Establishing governance frameworks for autonomous systems, defining roles, responsibilities, and approval authorities
- Developing policies that define acceptable use, escalation procedures, data handling protocols, and audit requirements for government operations
- Addressing compliance obligations and establishing evidence collection standards for regulatory audits
Non-Human Identity and Authentication Protocols
- Architecture of machine identities, including service accounts, JSON Web Tokens (JWTs), and ephemeral credentials
- Implementing least-privilege access models and just-in-time credential provisioning
- Managing identity lifecycles, including rotation, delegation, and revocation processes for government systems
Access Controls, Secrets Management, and Data Protection
- Deploying fine-grained access control models and capability-based authorization patterns for agents
- Securing secrets, enforcing encryption in transit and at rest, and applying data minimization principles
- Safeguarding sensitive knowledge bases and Personally Identifiable Information (PII) against unauthorized agent access
Observability, Auditing, and Incident Response
- Designing telemetry frameworks for agent behavior, including intent tracing, command logging, and data provenance
- Integrating Security Information and Event Management (SIEM) systems, defining alert thresholds, and ensuring forensic readiness
- Developing runbooks and playbooks for managing agent-related incidents and executing containment measures
Red-Teaming Autonomous Systems
- Planning red-team engagements, defining scope, rules of engagement, and safe failover mechanisms
- Evaluating adversarial techniques such as prompt injection, tool misuse, chain-of-thought manipulation, and API exploitation
- Executing controlled attacks to measure system exposure and potential impact on government missions
System Hardening and Mitigation Strategies
- Implementing engineering controls, including response throttling, capability gating, and sandboxing environments
- Applying policy and orchestration controls, such as approval workflows, human-in-the-loop validation, and governance hooks for government oversight
- Deploying model-level defenses, including input validation, canonicalization, and output filtering mechanisms
Operationalizing Secure Agent Deployments
- Utilizing deployment patterns such as staging, canary releases, and progressive rollouts for agent systems
- Managing change control, testing pipelines, and pre-deployment safety verification checks
- Facilitating cross-functional governance coordination among security, legal, product, and operations teams
Capstone: Red-Team / Blue-Team Exercise
- Conducting a simulated red-team attack against a sandboxed agent environment
- Defending, detecting, and remediating threats as the blue team utilizing established controls and telemetry data
- Presenting findings, remediation strategies, and recommended policy updates
Summary and Next Steps
Requirements
- Demonstrated expertise in security engineering, system administration, or cloud operations
- Working knowledge of artificial intelligence/machine learning (AI/ML) principles and the operational dynamics of large language models (LLMs)
- Proficiency in identity and access management (IAM) frameworks and the implementation of secure system architectures
Audience
- Security engineers and red team operators
- AI operations and platform engineering personnel
- Compliance specialists and risk management professionals
- Engineering leadership overseeing agent deployments
21 Hours
