145 posts on platform & architecture.
Zero trust applied to AI means evaluating every model request against verified identity, current policy, and prompt-level classification. The architectural pattern is an enforcement proxy at the HTTP AI request boundary. The post-authentication gap is the most common failure mode in current deployments.
Mandiant M-Trends 2026 measured median attack handoff at 22 seconds. At that tempo, log-and-alert fails as a control. Inline enforcement at the AI request boundary makes the policy decision before the request reaches the model. Under 50 ms enforcement overhead is invisible against 500 ms to 5 second model inference.
Model guardrails are trained behaviors inside the inference process. They degrade under fine-tuning, adversarial prompting, and role-play framing. External enforcement at the AI request boundary produces deterministic controls and identity-bound audit records that guardrails alone cannot.
NIST Pillar 1 names verified agent identity as the foundation of the AI agent identity and authorization framework. Per-agent identifiers, delegated authority from the authorizing user, and structured propagation to the model API call are the production requirements. Static service credentials fail the test.
AI inline enforcement runs the policy decision in the request path, before the model API call returns to the calling application. The architecture places a deterministic policy decision point between the application identity and the model endpoint and commits a per-decision audit record before the response forwards. This piece walks through the architectural components, the decision-time data shape, the failure modes the implementation has to handle, and the regulatory profile that the inline placement satisfies (EU AI Act Article 12, NIST AI agent identity and authorization Pillar 2 and Pillar 3, Fannie Mae LL-2026-04, DORA Article 6).
An Amazon Bedrock gateway sits between calling applications and the Bedrock runtime endpoints, attaches identity context to every InvokeModel and InvokeModelWithResponseStream call, evaluates a per-request policy, and commits a per-decision audit record before the request reaches Anthropic, Mistral, Meta, Cohere, AI21, or Amazon Titan. The gateway pattern complements Bedrock Guardrails by adding identity-bound policy enforcement and a per-decision audit record format that satisfies EU AI Act Article 12 and the Fannie Mae LL-2026-04 lender record requirement. This piece walks through the AWS SigV4 handling, the model-agnostic policy, and the audit record format.
An Anthropic API gateway sits between calling applications and api.anthropic.com, attaches identity context, evaluates a per-request policy, and commits a per-decision audit record before the request reaches Claude. The gateway pattern addresses the Anthropic Messages API, the tool-use loop, the streaming response, and the prompt caching feature. This piece walks through the request rewriting pattern, the system-prompt evaluation, the tool-use policy, the streaming SSE handling, and the audit record format that satisfies EU AI Act Article 12 and the deployer obligations under Article 26.
An OpenAI API gateway sits between calling applications and api.openai.com, attaches identity context, evaluates per-request policy, and commits a per-decision audit record before the request reaches the model. The pattern replaces the direct calling convention that uses an organization-bound API key with an inspection layer that the application addresses instead. This piece walks through the request rewriting pattern, the SSE and streaming response handling, the function-calling and tool-use evaluation, and the audit record format that satisfies EU AI Act Article 12 and the deployer obligations under Article 26.
A stateless AI proxy makes the policy decision on the contents of the current request and the per-decision audit record alone. A stateful AI proxy carries session memory, caches conversation history, or stores prompts across requests in its own storage. The choice has direct consequences for horizontal scaling, blast radius under compromise, the EU AI Act Article 12 record-keeping obligation, and the DORA third-party risk profile of the inspection layer. This piece walks through the architectural distinction, what each option requires from the deployment, and where most production teams settle once the trade-offs are visible.
Per-route AI policies attach a different enforcement rule to each LLM endpoint behind the inspection layer. A request to the customer-support route runs under one policy. A request to the developer-tooling route runs under another. The implementation lets a single inspection layer serve every team without the lowest common denominator policy that an organization-wide rule produces. This piece walks through the data model, the matching algorithm, the policy state that has to be present at decision time, and the operational characteristics that hold up at production scale across OpenAI, Anthropic, Azure OpenAI, and Bedrock endpoints.
A signed audit log binds a cryptographic signature to each record at the moment the record is committed. For AI requests, the signature ties the record to the inspection layer that produced it and lets a verifier confirm authenticity without trusting the storage layer. The technique is the cryptographic foundation under tamper-evident audit trails the EU AI Act Article 12, Fannie Mae LL-2026-04, HIPAA, DORA, and NIST AI agent identity framework all expect. This piece walks through the signing schemes, the key management, and the verification flow that auditors and regulators will accept.
Tamper-evident audit logs make any post-hoc modification of a record detectable through cryptographic integrity. For AI compliance records, the property closes the self-attestation gap that application-controlled logs cannot. The technique combines per-record signing, hash chaining, and external anchoring. EU AI Act Article 12, Fannie Mae LL-2026-04, HIPAA, DORA, and NIST AI RMF all expect records that an auditor can rely on as evidence. Application logs that the application can modify do not meet that standard. This piece walks through the cryptographic mechanisms, the operational characteristics, and the architectural placement.