Data Classification for the Claude Agent SDK

Uncontrolled AI agents can leak confidential data in seconds.

The Claude Agent SDK lets developers embed a powerful LLM into automation workflows. The SDK can read from databases, call internal APIs, and generate free‑form text that may contain whatever it has seen. When the underlying data includes personally identifiable information, secrets, or regulated records, the output becomes a compliance liability.

Data classification is the practice of assigning a sensitivity label to each data element, such as public, internal, confidential, or restricted, and defining handling rules for each label. In a traditional application, developers can enforce those rules in code, but an LLM‑driven agent does not follow static code paths. It can synthesize responses that inadvertently expose a confidential field or embed a password in a log.

Because the Claude Agent SDK operates at runtime, the only reliable way to honor data classification is to inspect the actual data flow as it happens. Static analysis cannot guarantee that a prompt or a generated response will respect the policy, and manual review of every LLM output is impractical at scale.

Why data classification matters for the Claude Agent SDK

The risk surface expands the moment an LLM is given direct access to production resources. A seemingly harmless query like SELECT * FROM customers can return rows that contain social security numbers, credit‑card digits, or internal project codes. If the agent then includes those rows in a generated report, the organization may violate privacy regulations or expose trade secrets.

Applying data classification consistently requires three things:

• A source of truth that records the classification of each column, field, or attribute.
• A runtime enforcement point that can see every request and response.
• Auditable evidence that the policy was applied.

Without a dedicated enforcement point, the Claude Agent SDK would have to rely on the developer to write custom filters for every possible response, a brittle approach that quickly breaks as the SDK evolves.

How hoop.dev enforces data classification

hoop.dev provides the missing runtime enforcement point. Identity is handled upstream via OIDC or SAML; the gateway verifies the token and determines which user or service account is invoking the Claude agent. That identity check decides who may start a session, but it does not enforce the classification policy.

When a request from the Claude Agent SDK reaches a target, such as a PostgreSQL database or an internal HTTP API, hoop.dev sits in the data path. It inspects the wire‑protocol payload, matches any fields that have been marked as confidential or restricted, and applies inline masking before the data is returned to the SDK. The masking is performed in real time, so the LLM never sees the raw value.

Because hoop.dev records each session, the system also generates a complete audit trail. Every request, the classification decision, and the masked response are logged. This evidence can be used for post‑mortem analysis or to satisfy audit requirements.

To make the enforcement work, administrators define a classification policy in hoop.dev’s configuration. The policy references the same data dictionary that the organization uses for its data governance program. Once the policy is loaded, any Claude Agent SDK call that touches a classified field is automatically subject to the mask, regardless of how the SDK constructs the query.

Practical steps to protect Claude‑driven workflows

1. Catalog the sensitivity of each data element in your critical stores. Tag columns, JSON keys, or API fields with the appropriate classification label.

2. Load those tags into hoop.dev’s policy engine. The documentation explains how to map classification tags to masking rules.

3. Deploy hoop.dev as the Layer 7 gateway for the resources the Claude Agent SDK will access. The quick‑start guide walks you through a Docker Compose deployment that includes OIDC authentication, masking, and session recording.

4. Point the Claude Agent SDK at the hoop.dev endpoint instead of the raw database or API host. The SDK continues to use its normal client libraries, but all traffic is routed through the gateway.

5. Verify that masked fields are never returned to the SDK by reviewing the audit logs in hoop.dev’s UI or exporting them for external analysis.

By following these steps, teams can let the Claude Agent SDK automate complex tasks while guaranteeing that classified data never leaves the controlled environment in clear text.

For a hands‑on walkthrough, see the getting‑started guide. The learn section provides deeper coverage of masking policies and audit reporting.

Explore the source code and contribute on GitHub.