Selah Beck

Multiple detection layers catch both explicit and semantic injection attacks in RAG systems.

Security teams now pair OWASP threat lists with NIST governance to audit AI vendors continuously.

OWASP and NIST findings feed one supplier risk program when mapped as evidence.

Securing AI agents requires isolation between instructions and data before models gain tool access.

The standard certifies governance systems, not model performance or fairness.

Regulators now require detailed AI documentation that most companies still aren't providing.

AI systems violate the three core assumptions traditional vendor risk assessment depends on.

Companies need named owners for each AI system, not just policies on paper.

Approval checkpoints alone won't stop rogue AI agents from causing real-world harm.

Attackers exploit how AI agents trust tool descriptions without checking if they change mid-session.

LLMs need their own security category because traditional tools can't stop prompt injection attacks.

Enterprises can't patch prompt injection, but three-layer defense reduces the damage.

Annual vendor reviews cannot keep pace with AI systems that change weekly or daily.

Layered defenses reduce prompt injection risk, but no single pipeline can eliminate it entirely.

Two overlooked attacks can steal a model's secret instructions through different methods.

Stacking four detection layers beats relying on any single method to stop prompt injection attacks.

Contracts must account for AI's shifting outputs, data loops, and shared liability chains.

Enterprises need new rules to spot AI features hidden inside trusted vendor software.

System prompts leak secrets because models can't distinguish instructions from user input.

Logging AI prompts captures personal data companies didn't mean to store.

RAG systems treat retrieved data as instructions, turning documents into attack vectors.

System prompts leak easily, and what's inside matters more than secrecy itself.

Fine-tuning on sensitive data multiplies privacy leakage rates up to seventy-five percent.

Treating model output as trusted data opens exploitable pathways to browsers, databases, and shells.

Hallucinations and model drift pose bigger risks than security breaches most organizations ignore.