Topic starter
11/06/2026 11:38 pm
TL;DR: Agentic, multimodal AI changes the risk model because systems can now act across text, images, and voice, where one poisoned input or spoofed instruction can trigger approvals, transfers, or policy decisions, according to Enkrypt AI. The governance gap is no longer model quality alone but whether runtime controls can constrain action before auditless damage occurs.
Editorial analysis by NHI Mgmt Group, based on content published by Enkrypt AI: “Enkrypt AI Recognized as a Gartner® Cool Vendor in AI Security 2025”.
Key questions
Q: What breaks when multimodal AI agents can act on screenshots, PDFs and voice prompts?
A: The control assumption that only text can drive high-risk agent behaviour breaks down.
Q: Why do multimodal AI agents increase the risk of unsafe approvals and transfers?
A: Because the agent can turn untrusted content into an execution signal without a human deciding whether the action is legitimate.
Q: How do security teams know whether agent guardrails are working?
A: They know guardrails are working when denied tool calls are visible in logs, high-risk paths are blocked consistently, and the agent cannot override policy from inside its own session.
Practitioner guidance
- Define action boundaries for multimodal agents Map which tool calls, approvals and record changes require explicit policy checks before execution.
- Gate high-risk outputs before downstream execution Block agents from triggering transfers, account changes or policy updates until a deterministic authorisation rule has passed.
- Harden input trust assumptions across media types Test whether a screenshot, PDF or voice sample can influence tool selection or approvals.
Bottom line: Multimodal AI agents can convert screenshots, PDFs and voice prompts into privileged actions, which expands the attack surface beyond text-only prompt abuse.
Explore further
View Full Forum → | NHI Foundation Course → | Our Services → | Read the full analysis →
12/06/2026 8:23 am
Runtime guardrails are becoming the control boundary for agentic AI. Once text, image and voice all feed the same action path, the question is no longer whether the model can reason correctly. The question is whether the organisation can stop an unsafe action before it reaches a payment, approval or policy engine. That makes pre-execution policy enforcement the real governance layer for multimodal agents.
A few things that frame the scale:
- Only 23% of IT leaders were very confident in their organisation's ability to manage security and governance for GenAI deployments, according to a 2025 Gartner survey of 360 IT leaders.
A question worth separating out:
Q: Should organisations treat agent content safety and action safety as the same control?
A: No. Content safety reduces harmful outputs, but action safety prevents those outputs from changing systems or authorising transactions. Multimodal agents need both, because a safe-looking response can still be converted into an unsafe operational step.
👉 Read our full editorial: AI agent guardrails are shifting from chat to multimodal action