This session demonstrates how quickly and easily enterprise-grade AI security can be applied to your Amazon Bedrock projects. See the new Prisma AIRS AI-security platform in action on AWS: We explore Model Scanning and AI Posture Management; then dive into Runtime Security; and finally examine automated AI Red Teaming and AI Agent Security. Unlock your AI-led innovation - securely.
What this session is about
Playbook
Editorial commentary · what to actually do about this on Monday
The concept
AI security splits into four distinct surfaces: model supply chain (scanning), configuration (posture), inference traffic (runtime), and adversarial probing (red teaming). Each needs different tooling.
Why it matters
Securing AI is not "put it behind a WAF." Prompt injection bypasses traditional perimeters. Models can leak training data. Tool-calling agents have privileges your APIs never granted.
The hard parts
Threat models for agents look different from threat models for APIs. Your existing playbook misses the new vectors.
Playbook moves
(1) Threat-model the agent's tool surface specifically. (2) Separate "data exfil via response" from "command injection via tool call" — they need different controls. (3) Schedule automated red-team runs into the release pipeline.
The surprise
The riskiest agent vulnerability isn't prompt injection — it's *authorisation confusion*. The agent inherits user identity for context but operates with system privileges for execution. Whoever can manipulate the prompt can effectively act as the system. This is the agent-era equivalent of SSRF, and almost no one is testing for it.
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Independent editorial perspective — not an official AWS or speaker statement. Designed for executives evaluating what to brief their teams on next.
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