Move from policy to real-time security enforcement before your AI infrastructure is compromised.
As federal agencies, local governments and universities rush to operationalize large language models (LLMs) and containerized AI applications, legacy security controls are failing. Firewalls can't identify prompt manipulation and scanners can't stop autonomous agents from exceeding their intended permissions.
Read this whitepaper to explore the core pillars of an effective AI defense: comprehensive ecosystem discovery, strict identity security for machine principals and live behavioral guardrails that deny unauthorized intent at machine speed.
What you’ll learn:
- The Technical Baseline: How to conduct comprehensive asset discovery across sanctioned deployments, shadow AI tools, and third-party model libraries.
- Controlling Agentic Autonomy: Strategies for enforcing least-privilege permissions and preventing lateral movement across connected enterprise systems and toolchains.
- Audit Readiness & Compliance: Align with strict federal guidelines, NIST AI Risk Management frameworks and CMMC data protection requirements to safeguard funding eligibility.
