AI DATA PROTECTION
Protect what enters model context.
Inspect sensitive information in AI requests and tool outputs. Apply policies that fit the data, the workflow, and the supported traffic path.
SENSITIVE DATA BECOMES MODEL CONTEXT
Protect the information
inside the workflow.
Logs, source files, support records, and tool outputs can carry data an AI task does not need to share.
Milgram inspects routed AI traffic for sensitive information, including PII, credentials, and authentication tokens. A policy can replace supported sensitive spans with placeholders before the request reaches the model provider.
This is useful when an assistant needs the structure of a record, error, or conversation without the original sensitive value. It also gives security teams a record of detections to investigate and refine.
Masking reduces exposure along the supported traffic path. It does not revoke a credential, remove a secret from its source system, or guarantee discovery of every sensitive value.
ILLUSTRATIVE WORKFLOWS
Different data. Different policies.
| Workflow | Risk to evaluate | Policy approach |
|---|---|---|
| Coding assistant | A diagnostic log includes an authentication token. | Mask the detected token in the provider-bound request. Investigate whether it also needs rotation at the source. |
| Customer support | A ticket includes personal details unrelated to the answer. | Mask applicable PII and verify that the assistant still resolves the intended task. |
| Agent investigation | A tool result returns credentials or private configuration. | Inspect the result when it re-enters model context, preserve investigation evidence, and apply the supported policy for that path. |
ROLLOUT
Validate data handling
before expanding access.
Identify sensitive inputs
List the systems, documents, tool outputs, and data types in the chosen workflow. Define what is necessary for the model to receive.
Observe and review
Use shadow mode to inspect matches and false positives on representative traffic. Include normal identifiers that could resemble secrets.
Test the masked workflow
Check structured payloads, streaming, downstream parsing, and task completion. Measure whether substituted values change the result.
Align evidence access
Restrict who can inspect original traffic and who may change policies. Provider-bound masking does not mean original content is absent from Milgram’s records.
Data protection questions.
Does masking mean Milgram never sees the original value?
No. Inspection occurs on original client content; outbound masking changes what is forwarded. Treat Milgram’s session and audit data as sensitive and evaluate access, retention, storage, and deletion for your deployment.
Can I use different policies for different teams?
The policy model supports variation by user and use case, alongside detection type and severity. Work with the team to map the organization’s boundaries and verify the intended precedence during an evaluation.
Does this make an AI workflow compliant?
A data protection control can support your governance program. Compliance also depends on contracts, lawful processing, access controls, retention, provider terms, and your operating procedures. Milgram does not present masking as a compliance certification.
INVITE-ONLY BETA
Bring your workflow. Define your evaluation.
Tell us what you use, what you need to protect, and where Milgram would run.