Stop Sensitive Data Leakage in AI
Detect and block sensitive data across AI interactions.
AI Creates New Paths for Data Exposure
Prompts, retrieval context, and tool calls can bypass the controls that normally protect sensitive information.
Copy-Paste Exposure
Users paste customer, employee, or internal data into prompts to “work faster,” bypassing approved handling paths.
Restricted Data in Context
RAG can pull restricted documents into context without consistent scoping by role, app, or environment.
Secrets in Prompts
Credentials and tokens can show up in prompts, outputs, or tool parameters and get retained in logs or downstream systems.
Agents Spread Errors Fast
Agents move data through tool calls and exports, but it is hard to see exactly what was sent and why it was allowed.
Detect PII Across Responses and Tool Payloads
Responses and tool payloads are checked for PII and recorded as a redaction record.
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Policy-Based Controls for AI Data Handling
Apply data-handling policy to AI interactions and take configurable actions when policy is met or violated.
Data-Handling Laws
Define what sensitive data is allowed, where, and under what conditions, scoped by app, route, role, model, and tool.
Sensitive Data Evaluations
Use patterns, similarity, classifiers, or model-based validation to match latency and rigor needs.
Scoped Runtime Actions
Monitor, redact, block, reroute, or escalate based on policy scope and severity.
Investigation-Ready Evidence
Retain what was evaluated, what matched, and what action occurred so teams can reconstruct and review exposure.
Prevent AI Data Exposure
Reduce exposure across prompts, retrieved context, and tool payloads with runtime actions.
PII Protection
Detect and redact identifiers in prompts and outputs when the route or workflow isn’t approved for PII.
Secrets Filtering
Block or redact API keys, tokens, and passwords before they reach users, logs, or downstream systems.
Proprietary Data Controls
Prevent internal documents and restricted content from appearing in context or responses outside approved scopes.
RAG and Tool DLP
Apply DLP to retrieval snippets and tool inputs/outputs so restricted fields don’t flow through connectors or exports.
DLP for AI Interactions
Traditional DLP lacks semantic and workflow context. ThirdLaw evaluates meaning to take the right action.
Context and Action Aware
Evaluate prompts, outputs, retrieval context, and tool payloads so enforcement matches real data flow.
Scoped Decisions
Scope policy by app and workflow context so what’s allowed is always situation-specific.
Multiple Evaluation Engines
Use patterns, similarity, classifiers, or model-based validation to match latency and rigor needs.