AI Security Posture Management. See the whole model supply line.
Every model your business runs has a history: the data it learned from, the permissions it holds, the endpoint it serves, and the apps that call it. AI-SPM gives you continuous visibility across that line, so the risk hiding between the steps has nowhere to sit.
Cloud posture tools were built for servers and storage. They do not understand a model's training data, its system prompt, or the standing permissions it carries into production. AI is an asset class your posture program cannot yet see.
Teams ship models faster than security can inventory them. The exposure is rarely in one place. It shows up as unmasked data in training, an over-permissioned service account at the endpoint, or prompt logging that quietly captures secrets. AI-SPM watches the full lifecycle and weighs each step against the others, so a small issue on a path to sensitive data is treated as the risk it is.
Follow one model, end to end
AI-SPM traces every model from the data it learned to the apps that call it, and flags the posture risk at each stage. Here is what one line looks like under watch.
Training data
The datasets and sources the model learned from
Training & tuning
Pipelines, notebooks, and the compute that built the model
Model registry
Versions, lineage, and who can promote a model to production
Inference endpoint
The serving layer and the identity it runs under
Apps & Agents
The products, Agents, and users that consume the model
Illustrative lineage for one model, not a claimed result. Real findings depend on your pipelines, permissions, and serving setup.
Posture across four fault lines
Most AI exposure falls into one of four categories. AI-SPM keeps continuous eyes on all of them and connects findings across the lifecycle.
Data exposure
Sensitive or unmasked data in training sets, fine-tuning data, and retrieval sources.
Permissions & identity
Over-permissioned model identities and service accounts with standing access to production.
Prompt & output risk
Prompt injection exposure, secret leakage, and logging that captures sensitive content.
Supply chain
Untrusted base models, plugins, and dependencies pulled into the pipeline.
Inventory & drift
Shadow models that never went through review, and posture that drifts after launch.
Control evidence
Proof that guardrails, logging, and oversight are in place and working, ready for audit.
The bridge between the cloud and the audit trail
Where the models run
The infrastructure, identities, and configuration that AI-SPM watches in motion.
Where the audit trail lives
The policy, inventory, and evidence that proves every model is governed.
Where AI-SPM sits in VIGILE
Identify the AI estate, Guard each model
AI-SPM runs through the Identify and Guard motions of VIGILE, discovering every model and the line it sits on, then hardening the data, permissions, and endpoints around it. The findings feed AI Governance and the EU AI Act program with evidence.
Explore AI Governance ›Top 10 questions, frequently asked
Cloud posture management understands servers, storage, and network configuration. AI-SPM understands the model: its training data, its registry and promotion rights, its serving identity, and its prompt exposure. It speaks the language of the AI lifecycle, which a general cloud posture tool does not. We run AI-SPM alongside your cloud posture program so the two cover different ground.
AI-SPM is the technical visibility layer, and AI Governance is the policy and accountability layer. AI-SPM finds the over-permissioned endpoint or the unmasked training data; AI Governance records the decision, sets the guardrail, and keeps the audit trail. The inventory and posture findings flow straight into the governance program, so the same work serves both security and compliance.
Yes. Shadow AI is one of the first things AI-SPM surfaces: models stood up by a team, endpoints spun up for a pilot, or third-party AI wired into a product without review. Discovery runs continuously, so a model that appears next month shows up rather than waiting for the next audit.
Yes. Whether you train your own models, fine-tune open ones, or call a managed provider, AI-SPM maps how the model is wired into your environment: the data it touches, the identity it runs under, and the apps that call it. Much of the risk is in that wiring, which is the same regardless of who built the underlying model.
The inventory, classification inputs, and control evidence AI-SPM produces map directly into the documentation those programs require. A continuously maintained posture picture is far easier to attest to than a point-in-time spreadsheet, so AI-SPM makes the compliance program faster and the evidence current.
The posture of every AI deployment: exposed endpoints, over-permissive keys, models reachable without authentication, training data exposure, and drift between the approved configuration and what is actually running.
A first posture readout typically lands within the first weeks, starting from cloud and SaaS integrations you already have. Coverage deepens as code and pipeline integrations connect.
It is routed to the named owner with context and a proposed fix. High-confidence, low-risk corrections can be automated; anything consequential goes through review.
As a posture trend, not a raw finding list: what is exposed, what changed, what was closed, and what is accepted with reasoning on record. The same dataset feeds audit evidence.
It is the Guard and Identify motions applied to AI infrastructure, running continuously, with its evidence feeding the quarterly Enhance readout.
Related work
Find the risk hiding between the steps
Book a session with a Principal Engineer. We map one model end to end, from training data to the apps that call it, and show you where the posture risk sits.