AISPM (AI Security Posture Management)
AI Security Posture Management (AISPM) is the discipline of discovering AI usage across an enterprise, classifying that usage by data sensitivity and regulatory exposure, and remediating the gaps in policy, identity, and audit coverage. AISPM borrows the inventory-first pattern from CSPM (cloud) and DSPM (data) and applies it to AI traffic, models, agents, and the prompts they handle. AISPM is the discovery and reporting layer that sits above the AI gateway's per-request enforcement.
What AISPM measures
The IBM Cost of Data Breach Report found that one in five breached organizations of the 600 studied experienced breaches linked to shadow AI, and those breaches cost $670,000 more on average and took 247 days to detect. AISPM surfaces the inventory that closes that detection gap: which models are being called, which roles are calling them, which routes carry PII or PHI, which agents have access to which tools, which API keys exist for which providers. The Cloud Radix finding that 86% of IT leaders are blind to AI interactions is what AISPM is built to fix.
How AISPM connects to enforcement
AISPM is the diagnostic layer. The AI gateway is the enforcement layer. Posture findings without an enforcement point produce reports and remediation tickets. The 97% of organizations that suffered AI-related breaches and lacked proper access controls (Netwrix, 2026) had postures that flagged the gap, but no inline control to close it. AISPM tells the CISO which routes need a policy. The AI gateway is where the policy lives at request time. Mature programs run both layers and feed AISPM the per-decision audit records the gateway produces, so posture reports describe what actually happened rather than what was inventoried.
Related reading
- Shadow AI Monitoring: What You Can Actually See and Where the Inspection Layer Has To Sit
Most shadow AI monitoring stops at the DNS layer or the CASB. Both miss the actual data leaving the organization because the prompt is the data, and the prompt sits inside an encrypted POST body. This piece walks through the four monitoring layers, what each one sees, where each one is blind, and the inspection architecture that produces evidence an EU AI Act or HIPAA auditor will accept.
- Shadow AI Risks: Quantified Loss Exposure, Regulatory Liability, and the Per-Incident Math
Shadow AI risk lives in three separate ledgers: the per-incident breach cost, the regulatory liability that attaches to the deploying organization regardless of which employee pasted what, and the contractual liability already shifting from AI vendors to enterprises. This piece walks through each ledger with the numbers from IBM, the EU AI Act, Fannie Mae, and Gartner, and shows where the architecture closes the exposure.
- AI Inline Enforcement Architecture: Where the Policy Decision Sits and What It Has To Commit
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).