IAI doesn’t just expand your capabilities—it expands your attack surface. To stay ahead, treat AI security as a layered discipline: not just securing infrastructure, but also safeguarding models, data pipelines, decision outputs, and even public trust. Begin by identifying where AI touches sensitive operations, then assess risks across the full lifecycle—from training data to deployment.

The Six Layers Framework™ helps you embed security at every level:

• Layer 1 (Infrastructure): Ensure compute, storage, and cloud environments are hardened for AI workloads. This includes secure data pipelines, access controls, and monitoring.

• Layer 2 (Models): Implement model-specific defenses: adversarial testing, input validation, and drift monitoring to prevent exploits.

• Layer 4 (Services): Build AI risk management into your operational model: establish oversight teams, red-teaming protocols, and vendor audits to assess third-party exposure.

• Layer 6 (The State): Track evolving regulations, export controls, and national standards. In many sectors, failing to secure AI systems will become a compliance failure—not just a technical one.

The Framework ensures your AI security strategy isn’t reactive or siloed—it’s structural. By layering defenses from hardware to policy, you reduce the risk of unintended consequences or targeted misuse—and position AI as a trusted part of your mission-critical stack.