Govern enterprise AI usage without forcing teams back to shadow workflows.
AI Access Security places policy between users and models so organizations can control which services are approved, what data can be shared, and how AI interactions are logged for review.
AI adoption is moving faster than enterprise control boundaries.
Teams want model access immediately. Security, legal, and compliance teams need visibility into what is being used, which data is being shared, and whether approved policy is actually enforced.
Blocking AI outright is rarely realistic. But unmanaged access creates exposure across sensitive data handling, third-party model usage, audit readiness, and policy drift between teams.
AI Access Security is intended to give enterprises a practical control point: one place to define allowed model usage, apply data handling controls, and preserve reviewability as adoption expands.
Visibility gap
Most organizations cannot clearly answer which teams use which models for which workflows.
Data handling risk
Sensitive prompts, source code, customer data, and internal knowledge may be shared outside approved boundaries.
Audit pressure
Review-heavy environments need evidence that AI usage is controlled, documented, and policy-aligned.
Enforce policy at the AI access layer.
The platform is positioned as a control and review layer between enterprise users, approved workflows, and selected model providers or private deployments.
Policy enforcement
Define who can use which models, for what purpose, and under which handling rules.
Prompt and response controls
Apply inspection, redaction, routing, or handling logic before traffic reaches a model endpoint.
Audit and review support
Preserve usage records and policy context so reviewers can assess how AI is being adopted across the organization.
Useful when AI enablement and AI governance have to move together.
AI Access Security is typically relevant for regulated teams, internal platform groups, and organizations trying to standardize approved AI usage patterns before they sprawl.
Primary use cases
Model allow-listing, data redaction, AI usage logging, internal AI gateway controls, and departmental access policy.
Evaluation questions
Which models are approved, what data classes require protection, and what evidence is needed for security or compliance review?
Deployment posture
Fit depends on traffic architecture, identity integration, logging requirements, and the degree of customer-controlled deployment needed.
Enable AI usage with controls that can survive review.
Tell us which models you need to govern, what data boundaries matter, and how your security and compliance teams evaluate new control layers.