Protecting Sensitive Data in Casework Agents

Casework agents help staff review applications, eligibility, and case histories, pulling Social Security numbers, medical records, and financial data into prompts. File and email DLP tools do not inspect these interactions, so citizen data can reach unapproved models or the wrong user. PointGuard AI applies AI-native data protection at each AI interaction point, keeping citizen data inside approved boundaries.

Citizen data now moves through AI channels that agency DLP was not built to inspect.

• Caseworkers paste case notes into AI assistants.
• Agents retrieve full records when only a field is needed.
• Responses can surface another applicant's information.
• Privacy Act, HIPAA, and IRS rules apply to different data types.

AI Data Protection, Runtime Guardrails, and MCP Security Gateway enforce data policies across casework AI.

1. Define protected data. Use built-in detectors for SSNs, PHI, and financial data, and add agency-specific case and benefit identifiers.

2. Inspect every interaction. Scan prompts, responses, and governed tool calls at sub-100 ms latency.

3. Redact or block exposure. Mask sensitive fields when work can safely continue, and block data headed to unapproved models or users.

4. Record evidence. Log detections and actions for privacy officers and incident response teams to support breach assessment and audits.

• OWASP Top 10 for LLMs: LLM02 Sensitive Information Disclosure and LLM06 Excessive Agency, covering citizen data disclosed through AI and agents retrieving more than they need.
• OWASP Top 10 for Agentic Applications: ASI02 Tool Misuse and Exploitation and ASI03 Identity and Privilege Abuse, addressing agents that misuse tools or access to move citizen data.
• NIST AI RMF: MAP identifies sensitive data flows; MEASURE evaluates exposure; MANAGE applies controls; GOVERN assigns privacy ownership.