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 →October 5, 2026
AWS Bedrock Security: Guardrails, IAM, and Privacy Gaps
AWS Bedrock security uses Guardrails for content, PrivateLink for a private API path, and IAM for API access. This guide shows what each control covers, what Bedrock privacy settings govern, and where per-request authorization remains with the application or a separate policy layer.
October 5, 2026
DORA Third-Party Risk for AI: What ICT Third-Party Providers Have to Show
DORA took effect January 17, 2025. The regulation treats AI vendors as ICT third-party service providers. Financial entities must maintain a register of contractual arrangements, monitor concentration risk, and demonstrate exit strategies. AI inference sits squarely inside the obligation.
October 5, 2026
Authorizing Individual MCP Tool Calls at Invocation Time
MCP tool authorization evaluates each tools/call name and arguments object at the tool server. This guide explains how connection tokens differ from invocation policy, how argument checks constrain model-selected actions, and what evidence an authorization review needs.