AI security built on interpretability
AI security built on interpretability
What each category of AI security tool covers, where they share a blind spot, and six capabilities to evaluate before you buy.
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Why now
Teams run open-weight models, hosted APIs, and agents that read mail and call tools. Security owns that risk, often with limited visibility into what a model learned or how it behaves at inference.
$4.8B
Forecast spend on securing AI in 2027, up 68.7% from $2.8B in 2026.
50%+
Enterprises expected to use AI security platforms by 2028, up from under 10% in 2025.
1 in 2
Successful attacks on AI agents that will exploit access control or prompt injection by 2029.
Source: Gartner, 2026. Full citations in the guide.
The shared gap
Every category covers a real risk. Each one watches what a model says, how it is configured, or how people use it.
Gartner segment
AI usage control
Can't see: whether a model you host or call has been altered.
Gartner segment
AI gateways
Can't see: a model that misbehaves on a valid request.
Gartner segment
AI governance platforms
Can't see: what the model computes, only what teams attest.
App security · exposure
AI security posture management
Can't see: tampered weights in a correctly configured model.
App security · testing
AI security testing
Can't see: behavior trained into the weights, and triggers testers don't guess.
App security · runtime
AI runtime defense
Can't see: internal state. A model about to defect looks healthy until it acts.
Your checklist
Compare platforms on what each one can show you, not on how it describes itself. The guide pairs every capability with a question for your shortlist.
Sample vendor question · 01
If a model's weights, quantization, adapters, prompts, or sampling settings change after approval, how and when does your platform find out?
Verifies model integrity and provenance, and knows when to rescan
Surfaces misalignment and potential backdoors that output testing misses
Enforces one policy before a request reaches a model or tool
Detects, contains, and investigates at runtime, with detections you own
Covers the models you host and the AI services you consume
Produces compliance evidence as a byproduct of operations
Detections mapped to ATLAS
Checklist item 04 asks which MITRE ATLAS techniques a vendor's detections are tagged to. Here is our answer: the October detection pack adds 44 tested detectors, each mapped to the techniques it catches.
63
ATLAS techniques detected at runtime, up from 6
13/16
ATLAS tactics with runtime detection, up from 6
10/10
OWASP Agentic risks with an enabled detector
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Move from inferring what a model intends to measuring what it does. Start with the guide, or see the Starseer platform on your own models.