AI Agent Security, Privacy & Alignment
Concept & Motivation
An AI agent with access to your CRM, your documents, and your email is the most powerful — and most dangerous — tool you can deploy. The same capabilities that make agents useful (tool access, autonomy, reasoning) make them a security surface unlike anything IT has managed before.
Most organizations are deploying agents with security practices designed for traditional software. That’s a mistake. Agents reason, improvise, and take actions their designers didn’t anticipate. Security for agentic AI requires new thinking.
This course teaches that thinking — from threat models specific to AI agents to practical security architectures that enable deployment without creating unacceptable risk.
What You’ll Learn
- Agent-specific threat models — prompt injection (direct and indirect), tool misuse, data exfiltration through reasoning chains, privilege escalation via tool access. The threats that don’t exist in traditional software
- Data privacy architecture — what data flows to the model provider? What stays on your infrastructure? Data classification frameworks for agent inputs and outputs. GDPR implications of agent-processed personal data
- Access control for agents — least-privilege tool access, scoped permissions, sandbox environments. How to give agents enough access to be useful without giving them the keys to everything
- Alignment in practice — instruction hierarchy, system prompts, guardrails, output validation, behavioral boundaries. How to make agents do what you intend, not just what you literally say
- Self-hosted vs. cloud AI — security trade-offs of API-based models (OpenAI, Anthropic) vs. self-hosted (Ollama, vLLM). Data residency, latency, cost, and the hybrid architectures most enterprises adopt
- Audit and observability — logging agent reasoning chains, tool calls, and decisions. Building audit trails that satisfy compliance requirements. Monitoring for anomalous agent behavior
- Regulatory compliance — GDPR, AI Act, GxP (pharma), DORA/MaRisk (finance), NIS2 (critical infrastructure). What each requires for AI agent deployments and how to document compliance
- Incident response — what happens when an agent goes wrong? Containment, investigation, and remediation playbooks specific to agentic AI incidents
Who This Is For
- CISOs and security architects defining AI security policies
- Data protection officers assessing GDPR implications of agent deployments
- Compliance and risk managers in regulated industries
- IT directors responsible for infrastructure and access control decisions
- Technical leads implementing agent security in production
Participants should have a basic understanding of AI agents (Agentic AI Foundations is recommended but not required).
Format & Duration
2-day intensive (on-site). Day 1: threat models, data privacy, access control, and alignment with live attack demonstrations. Day 2: participants develop a security policy and architecture for an agent deployment in their own organization, including regulatory compliance mapping.
What Makes This Course Different
Security courses for AI typically focus on model training (data poisoning, adversarial examples) — relevant for ML researchers, irrelevant for enterprises deploying pre-trained agents. This course focuses on deployment security: the risks that emerge when you give an AI agent tools, data, and autonomy in your organization.
The threat demonstrations are real. You’ll see prompt injection attacks that bypass naive guardrails, data leakage through reasoning chains, and privilege escalation through tool chaining. Then you’ll learn the architectures that prevent them.