AISPM vs MLSecOps
AISPM and MLSecOps divide AI security at the line between runtime usage and the model build pipeline. AI Security Posture Management (AISPM) discovers and scores how an enterprise uses AI in production: which models get called, by which identities, carrying which data classes, under which policies. MLSecOps applies DevSecOps discipline to the pipeline that produces and ships models, covering training data integrity, dependency scanning, model registry access, artifact signing, and deployment gates.
Where each discipline operates
AISPM inherits the posture-management pattern from CSPM and DSPM: inventory first, score the gaps, drive remediation. Its findings read like exposure statements, an unsanctioned ChatGPT integration carrying customer records, an agent holding a provider key with no policy attached, a route where PHI flows without redaction. MLSecOps findings read like pipeline defects: a poisoned dataset in the training corpus, an unsigned model artifact promoted to production, a registry writable by the CI account. An enterprise that consumes vendor LLM APIs (OpenAI, Anthropic, Bedrock) carries a thin MLSecOps surface and a wide AISPM surface. An enterprise training its own models carries both in full.
How the two meet at the request layer
Posture findings without an enforcement point produce tickets, and Netwrix found that 97% of organizations that suffered AI-related breaches lacked proper access controls for AI services. The AI gateway is where both disciplines converge on evidence: its per-decision audit records feed AISPM with observed usage rather than declared usage, and the model version named in each record confirms whether the artifacts MLSecOps signed are the artifacts production actually served.
Related reading
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- Best AI Security Tools 2026: The Categories That Cover Different Layers and How To Choose
The "best AI security tools" list looks different in 2026 because the EU AI Act, Fannie Mae LL-2026-04, and DORA changed what regulated buyers actually need. The category splits into five product shapes covering different layers of the AI request path. This piece walks through each category, the obligation it closes, the failure mode that disqualifies a vendor in the category, and the fit pattern for a regulated stack.
- 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).