Zero-Trust enforcement for enterprise AI.
DeepInspect authorizes, redacts, or blocks every AI request inline, then writes a cryptographically signed forensic record for regulatory defense.
Four surfaces of the AI governance problem, one control plane.
The gateway sits in the request path with sub-50ms overhead, evaluates policy, and writes an evidence-grade record before the response is released.
Intelligent Security
Identity- and data-aware enforcement. Inline. Fail-closed.
Verifiable Governance
HMAC-signed forensic record for every decision.
Cost & Routing
Tier-based model routing, failover, per-token attribution.
Agent & MCP Governance
Per-tool policy and signed agent traces across every hop.
Who Owns AI Governance Liability in an Enterprise?
AI governance is a responsibility boundary. Regulators and boards want enforcement records, not dashboards.
The boundary falls on the enterprise rather than the model provider. Provider terms of service place contractual responsibility for how a model is used inside a customer environment on the customer. A regulatory inquiry about a specific AI decision reaches the CISO and the compliance officer, and the durable answer is a runtime record of how each AI request was handled together with the policy version in effect at the time.
Enterprises own:
Each obligation maps to an operational record only the enterprise can produce. Regulatory exposure resolves when the enterprise shows the specific decisions the system made and the policy versions in effect. Audit outcomes improve when the auditor reads records that each carry a per-record cryptographic signature verifiable on its own. Breach narratives hold together when every AI interaction in the sequence carries its own integrity proof. Board accountability follows the pattern of financial controls, signed attestations rather than best-effort summaries.
Governance requires reconstruction of who accessed what data and why a decision was allowed.
The risk lives in ungoverned AI usage inside your enterprise.
Point your AI client at the gateway. Nothing else changes.
Enforcement activates gradually from observe mode to enforce mode, per application and per data class. Application code stays intact.
Product
Inline enforcement between callers and models.
Identity-aware policy, deterministic evaluation, fail-closed by default. Sub-50ms overhead in the request path.
Forensics
Signed record for every AI decision.
Reconstruct any interaction. Verify integrity per record.
Architecture
Deterministic policy engine, default-deny posture.
Deploys inline. Payload-agnostic. Provider-neutral.
< 50ms
Gateway overhead
100%
Decisions signed
6+
Providers supported
HMAC
Per-record integrity
Recent thinking.
All posts →August 30, 2026
NIST 800-53 AI Compliance Checklist: 12 Evidence Tests
Use this 12-item NIST 800-53 AI compliance checklist to prepare endpoint, identity, policy, and audit evidence an assessor can test. Every item maps to a control identifier and defines a concrete completion check for the next assessment.
August 30, 2026
How to Detect Prompt Injection in Production
Detect prompt injection in production by inspecting inbound prompts, model outputs, and tool requests. Each layer catches a distinct path to an unsafe action, and the model-request and tool-authorization boundaries need separate evidence.
August 30, 2026
Embedding Leakage: 92% Text Recovery From Vectors
Embedding leakage can expose source text: a 2023 vec2text paper recovered 92% of short inputs exactly from embeddings and recovered patient names from clinical notes. The result changes how teams should classify and access-control vector stores in RAG systems.