Inference-time AI security gaps: what practitioners need to fix

TL;DR: 93% of AI leaders know traditional encryption stops protecting data in memory during AI inference, yet only half can fully verify protection while a model is running, leaving security, privacy and compliance gaps that delay or stop deployments, according to Fortanix. The real control problem is proving trust in use, not just encrypting data at rest.

Editorial analysis by NHI Mgmt Group, based on content published by Fortanix: “Enterprise AI Security: How to Protect Data, Models, and AI Agents”.

By the numbers:

• 93% of AI leaders are aware that traditional encryption stops protecting data when it is being processed in memory during AI inference.

Key questions

Q: How should security teams govern sensitive data used by AI systems?

A: Security teams should treat AI as a data consumer that needs policy boundaries, not just authentication.

Q: Why do AI deployments fail compliance reviews even when data is encrypted?

A: They fail when teams cannot prove that encryption remained effective while the model was actively processing sensitive data.

Q: What breaks when authority expands across multiple AI agents and tools?

A: Authority expansion breaks least privilege and weakens accountability.

Practitioner guidance

• Define inference as a protected runtime zone Map AI inference to a separate protection boundary with explicit controls for data in use, model weights and runtime access.
• Bind key release to attestation Release encryption keys only after trusted execution environment attestation confirms the workload is running in a policy-compliant state.
• Assign a single owner for agent governance Name one accountable team for agent permissions, monitoring and incident escalation so shared responsibility does not become no responsibility.

Bottom line: Enterprise AI security now depends on proving trust while data and models are in use, not only protecting them when they are stored or transmitted.

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