AI Security Runtime
Apply policy to prompts, responses, and tool actions. Block threats and protect sensitive data before it reaches a model.
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Legacy security was not built for AI
Built for networks, not language
Legacy DLP and firewalls match patterns. Prompt injection hides in ordinary words.
Blind to tool actions
Agents call tools and share files. Traditional controls never see the decision.
No evidence trail
When auditors ask what AI did with sensitive data, there is nothing to show.
Four steps on the governed path
Policies apply across the Secure Enterprise AI Workspace, gateway, API, and agents.
- 1Observe
Capture everything
Every governed interaction across surfaces.
- Workspace
- Gateway
- API
- 2Detect
Read in context
Semantic Security Engine weighs data, actor, destination, and intent.
- Threats
- Sensitive data
- 3Enforce
Apply the policy
Allow and record, redact in place, hold for review, or block.
- Redact
- Hold
- Block
- 4Trace
Keep the record
Surface, detection, policy, and action recorded as metadata.
- Decision trail
Threats stopped before the model. Evidence ready before the audit.
See AI activity across every team and model
One view of who is using which models, for what work, on which company data.
- Usage by team, user, and model
- Security activity in the same console
- Adoption signals for enablement
Policy decides what proceeds
Redact a sensitive span while the rest of the interaction continues. A held item waits for a designated reviewer.
- Allow, redact, hold, or block
- Checks before the model call and before delivery
- Always-block classes stay deterministic
Evidence for every decision
The decision trail records what happened without duplicating prompt or response bodies.
- Policy, detection, and action per event
- Decision timestamps for audit
- Reports for SOC 2, GDPR, and HIPAA
See the runtime on your own AI traffic
A 30 minute walkthrough with a security engineer.
It takes AI to secure AI
Specialist agents interpret each interaction in context, using company knowledge, permissions, and policies from your Security Context Graph.
- Semantic security
Infer intent
Distinguish instructions from content. Check intent against the authorized task.
- Semantic security
Classify sensitive data
Interpret entities and concepts using company knowledge and access rules.
- Semantic security
Reason about actions
Evaluate proposed tool calls and actions against identity, permissions, and policy.
Works with the models and tools you already use
- OpenAI
- Anthropic
- Google Gemini
- Meta
- Mistral
- xAI
- DeepSeek
- Cohere
- Microsoft 365 Copilot
- Copilot Studio
- SharePoint
- MCP tools
Questions buyers ask
Where does enforcement happen?
Policies apply across the Secure Enterprise AI Workspace, gateway, API, and agents. Enforcement is scoped to the surfaces and interactions on the governed path.
What happens when a policy fires?
The configured action allows and records, redacts in place, holds for human review, or blocks. A redacted request can continue without the sensitive span.
Are raw prompts retained as evidence?
The decision trail records metadata such as surface, detection, policy, and action, without duplicating prompt or response bodies.
How is it deployed?
Through the Secure Enterprise AI Workspace in the browser or desktop app, with the gateway and API for existing apps and agents. Most teams are live in under two weeks.
Solutions and services built on the runtime
One runtime on every AI interaction
One secure workspace for chat, agents, and company knowledge, with security and governance built in.
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- Observe
- Detect
- Enforce
- Trace