Chat with Protegrity
Curious about secure AI? Ask our agent.
Protect data.
Verify agents.
Govern inference.
AI teams can’t ship.
Security teams can’t take the risk.
AI teams
Can’t ship what they need to, fast enough.
- Forced to ship diminished agents
- Address security issues with every project
- Blocked from accessing the data they need
- Held up in security review and compliance
Security teams
Can’t enable AI and still contain risk.
- No way to make data private and usable
- No way to validate agent actions
- No way to govern model inference
- No way to provide regulators with evidence
Secure agentic systems, end to end.
Enterprise security was built to keep people away from sensitive data.
AI breaks the three assumptions it rests on.
Traditional security assumptions and why they fail for AI
Data is blocked because of compliance
Agents can’t function without data
Identity belongs to a person
Agents are not people, but may act for them
Knowledge is safeguarded by visibility
Models can infer what they haven’t seen
Protegrity addresses all three from one policy definition.
Data
Semantic encryption
Provides agents with protected equivalents to reason over.
Agent
Dual-principal trust
Authorizes the agent and the person directing it as a pair.
Inference
Guardrails and browser plugin
Prevents restricted knowledge from surfacing via inference.
Mapped to the frameworks security teams already use.
OWASP MITRE ATLAS NIST AI RMF
Prevent sensitive information disclosure
without waiting on a data request.
Semantic encryption creates protected equivalents that preserve meaning and relationships—unlike standard encryption, which makes data unusable, or basic redaction, which makes it unreferenceable.
Protegrity applies semantic encryption wherever sensitive data lives or flows:
- Stored in databases and data warehouses, through native integration
- Retrieved via MCP, SaaS applications or REST endpoints, through a gateway
- Entered by employees in chat prompts, through a browser plugin
Stop agent identity abuse and tool misuse
without descoping agent capabilities.
Protegrity makes agents registered, verifiable principals. Every tool call is permission-checked and logged in a tamper-evident record—capturing the agent and, when delegated, the person behind it—so teams see what an agent was blocked from doing, not just what it attempted.
Block inference attacks and re-identification
without making answers useless.
Protegrity governs model outputs the same way it protects inputs—inspecting what a model produces before it reaches the agent or the user, so protected data can’t re-emerge as an inference.
Drive growth
Reduce risk
Achieve compliance
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