Topic starter
25/10/2026 11:10 am
TL;DR: Zero trust still leaves AI runtime exposure unresolved, Fortanix says, because prompts, model weights and proprietary data are handled in memory during execution. Runtime trust shifts the decision point to the workload itself, where attestation and confidential computing can verify what is running before keys and data are released.
Editorial analysis by NHI Mgmt Group, based on content published by Fortanix: “What Comes After Zero Trust? The Answer is Confidential AI”.
Key questions
Q: Why does Confidential AI matter for security teams?
A: Confidential AI matters because it protects sensitive data and model workloads while they are being processed, not only when stored or transmitted.
Q: What are the signs that a company is relying too much on perimeter security for AI access?
A: A company is over-relying on perimeter security when AI agents already hold valid credentials, can reach multiple internal systems, and security confidence is weakest for machine-to-machine connections.
Q: How should security teams implement zero trust for workloads and AI agents?
A: Start by giving each workload or agent a verifiable runtime identity, then enforce request-level policy and issue short-lived credentials only after the identity and context checks pass.
Practitioner guidance
- Separate access approval from runtime assurance Document zero trust as the entry control and add a distinct control for workload execution so sensitive AI assets are not treated as safe once authentication succeeds.
- Require attestation before releasing sensitive keys Make key release conditional on verified enclave state so inference systems only receive secrets after the runtime environment passes trusted execution checks.
- Inventory where AI workloads handle sensitive material Map prompts, model weights, embeddings and outputs to the environments that process them, then flag shared GPU and hybrid deployments as higher-assurance workloads.
Bottom line: Zero trust still leaves AI runtime exposure unresolved, because authentication does not protect the point where prompts, model weights and outputs are processed.
Glossary terms: Confidential Computing, Runtime Attestation, Workload Identity
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This topic was modified 7 days ago by NHI Mgmt Group
27/10/2026 7:17 pm
Runtime trust is now the missing layer in zero-trust programmes. Identity controls can decide who may initiate a session, but they do not govern what happens once AI starts processing sensitive material. That means the most important control boundary for AI is no longer the login event, it is the verified execution environment. Practitioners should stop treating runtime protection as an optimisation and start treating it as a governance requirement.
A few things that frame the scale:
- 90% of IT leaders say properly managing NHIs is essential for a successful zero-trust implementation, according to the Ultimate Guide to NHIs.
A question worth separating out:
Q: Should organisations prioritise attestation or access control for AI systems?
A: They should treat them as linked controls, but attestation comes first when the goal is to protect sensitive in-use data. Access control decides who may request action, while attestation decides whether the environment is trustworthy enough to receive keys and datasets. Without proof of environment integrity, access control alone is incomplete.
👉 Read our full editorial: Confidential AI extends zero trust into runtime execution
This post was modified 7 days ago by NHI Mgmt Group