Protect data.
Verify agents.
Govern inference.
Chat with Protegrity
Curious about secure AI? Ask our agent.
AI projects end up delayed or stalled. AI ambition far exceeds governance.
Engineering
Can’t deliver what’s asked for, on time.
- 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
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
EMA State of AI Friction, 2026
View the survey results- 83%
- report security or compliance reviews delaying AI into production.
- 82%
- ship AI in a diminished state because they can’t clear the risk.
- 69%
- run agents that write to databases and trigger APIs without protection.
- 92%
- rate protection embedded within AI workflows as very valuable.
Proven protection, built for AI.
Enterprise security was built to keep people away from sensitive data. AI breaks the three assumptions it rests on. Protegrity addresses all three from one policy definition.
| Traditional security | Why it fails for AI | Protegrity solution |
|---|---|---|
| Data is blocked for compliance | Agents can’t function without data | Semantic encryption provides agents with protected equivalents to reason over. |
| Identity belongs to a person | Agents are not people, but act for them | Dual-principal trust authorizes the agent and the person directing it as a pair. |
| Knowledge is in people’s heads | Models can infer what they haven’t seen | AI guardrails prevent restricted knowledge from surfacing via inference. |
Swipe horizontally to see all three columns.
What the AI actually receives
Protected / tokenized| Field | Value in context | Consequence |
|---|---|---|
| Patient name | Kvrmtq Bldnsy | Protected · retains joins |
| Health card no. | 4471 882 019 | Protected · retains joins |
| Diagnosis code | E11.9 | Allowed context |
| Clinician note | patient [token] since [token] | Protected · retains joins |
| Address | 18 Kqvmt Cres, S4S | Protected · retains joins |
| Encounter date | 2026-05-14 | Allowed context |
Assemble the chart
The copilot retrieves 340 encounters with identity already tokenized at the source.
Summarise and reason
Consistent tokens allow patient records to remain linked across encounters.
Index for reuse
The example search index retains protected identifiers instead of readable identity.
Show privacy the receipt
An authorized access record can show which values were revealed and for what purpose.
Outcome · protected
In this illustrative configuration, the clinical summary uses protected identifiers while authorized users can retrieve the values they need under policy.
Illustrative values and workflow behavior for discussion only — not customer data, benchmark results, or a guarantee of a specific outcome.
Control exposure
before it happens.
Explore how AI agents can combine permitted answers into sensitive business knowledge, and how Protegrity helps control exposure before it happens.
Use everything. Expose nothing.
Caught at the gateway by policy that runs the same way every time, not a filter to talk around.
Mapped to AI security frameworks
OWASPMITRE ATLASNIST AI RMF
Prevent sensitive information disclosure without waiting on a data request.
Semantic encryption creates protected equivalents designed to preserve useful meaning and relationships while reducing exposure of readable sensitive data.
Protegrity can apply data protection wherever sensitive information 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. Tool calls can be permission-checked and logged with the identity and policy context behind each action.
Teams can review what was authorized, blocked or executed, rather than relying on the final result alone.
Block inference attacks and re-identification without making answers useless.
Protegrity can apply governance to model outputs as well as inputs, checking responses before information reaches an agent or user.
The goal is to reduce sensitive-data leakage while keeping approved answers useful.
One platform to control, enforce, and verify AI.
1Find and protect
SDKs for finding and protecting sensitive data in prompts, logs, and unstructured text.
Learn more2Semantic guardrails
Govern how agents retrieve data, reason over enterprise knowledge, and generate responses.
Learn more3Anonymization AI
Irreversibly de-identify sensitive data while preserving value for analytics and model development.
Learn more4Synthetic data
Generate realistic, privacy-safe data for testing, training, and simulation.
Learn more
Make your existing stack safe for AI
Built into the platform
your AI already uses.
Explore all integrationsCustomer Success
Protegrity’s Impact. Measurable by a Customer.
See how a top-five global bank used Protegrity to strengthen data protection while enabling cross-border data sharing, enterprise analytics, and new business opportunities.
$50M+
savings in infrastructure and data processor expenses.
$18M
net-new revenue over five years.
$1.2B
in sales by off-boarding commercial cards.
130
countries maintaining data sovereignty.
Customer Stories
Protection built for any industry.
“Protecting our customer’s PII data is essential. Protegrity tokenization accelerates our secure transformation while enabling advanced analytics with protected data.”
“Protegrity’s data protection solution is the bedrock of Accelya’s AI security strategy and protects our airline customer data each minute of each day.”
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