Zero-Trust Enforcement for Enterprise AI Usage.
DeepInspect embeds deterministic policy enforcement directly into AI request lifecycles, authorizing, modifying, or blocking actions before execution. It produces cryptographically verifiable forensic records for regulatory defense.
Pillar 1
Intelligent Security
Inline, fail-closed enforcement
Pillar 2
Verifiable Governance
HMAC-signed forensic record
Pillar 3
Cost & Routing Optimization
Tier-based routing and failover
Cross-cutting
Agent & MCP Governance
Per-tool policy, signed agent traces
Enterprise AI adoption has moved ahead of enterprise AI governance. Applications and agents issue requests to OpenAI, Anthropic, Azure OpenAI, Google Gemini, and self-hosted models against data spanning every sensitivity class, while the controls that should govern that traffic sit in policy documents, SIEM dashboards, and model-safety filters that run after the fact. DeepInspect inserts an enforcement layer in the request path. Every AI request arrives at the gateway, is evaluated against the active policy version, and clears or blocks synchronously. The decision and its inputs are written to a tamper-evident forensic store with a cryptographic signature.
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.
DeepInspect covers three surfaces of the AI governance problem. The product enforces policy inline before requests reach any model. Forensics holds the complete decision record for every interaction. The architecture places the gateway in the request path with sub-50ms overhead.
The gateway integrates quickly: applications point their AI client at the DeepInspect gateway URL and the gateway handles authentication, policy evaluation, and forwarding to the upstream model. Application code stays intact. Enforcement activates gradually from observe mode to enforce mode, per application and per data class, so AI adoption inside the enterprise stays on its existing trajectory while the compliance posture improves.
Product
Inline enforcement between users, agents, and models. Identity-aware policies with fail-closed behavior.
Forensics
Full reconstruction of every AI interaction. Cryptographically signed decision records for legal review.
Architecture
Built for security teams. Inline gateway deployment with deterministic policy engine and default-deny posture.