Authensor

Inside SafeClaw's Secrets Redaction Engine

Authensor Team · 2026-02-13

Inside SafeClaw's Secrets Redaction Engine

AI agents are incredibly useful — until they accidentally paste your AWS secret key into a public GitHub issue. Or send your database connection string to a third-party API. Or include a private SSH key in a log file.

This is not a hypothetical scenario. It happens, and it happens quietly. That's why we built SafeClaw's secrets redaction engine.

The Problem Space

AI coding agents interact with your filesystem, your environment variables, your configuration files, and your shell. All of these are treasure troves of sensitive data. An agent that reads .env and then makes an HTTP request has everything it needs to exfiltrate credentials — and it might do so without any malicious intent, simply because it was trying to be helpful.

Traditional secret scanning tools work on committed code in CI pipelines. That's too late. By the time a secret hits a commit, the damage may already be done. SafeClaw operates at the point of action — intercepting secrets before they leave your machine.

How the Redaction Engine Works

The secrets redaction engine sits in the data path between agent actions and external outputs. Whenever the agent produces content destined for a file write, a network request, a clipboard operation, or a log entry, the redaction engine scans it first.

Pattern-Based Detection — We maintain a curated library of regex patterns for known secret formats: AWS access keys, GitHub tokens, Stripe keys, JWTs, private keys, database URIs, and dozens more. These patterns are drawn from the same databases used by tools like truffleHog and detect-secrets, but optimized for real-time scanning. Entropy Analysis — Not all secrets follow predictable formats. For high-entropy strings that appear in sensitive contexts (like assignment to variables named password, secret, or api_key), we apply entropy scoring to flag potential secrets that patterns alone would miss. Context-Aware Redaction — The engine doesn't just find secrets; it understands where they came from. A string that looks like an API key in an .env file is treated differently than the same string in a test fixture. Context awareness reduces false positives while keeping detection aggressive where it matters.

What Happens When a Secret Is Found

When the engine detects a secret, it takes one of three actions depending on your configuration:

  • Redact — The secret is replaced with a placeholder like [REDACTED:aws-key] and the action proceeds. The agent continues working without ever seeing the real value.
  • Block — The entire action is denied, and the user is notified with details about what was found and where.
  • Escalate — The action is paused and sent to the approval queue, where a human can review the context and decide.

    • The default mode is redact, because it preserves the agent's workflow while eliminating the risk. Most agents don't actually need the real secret value — they just need to know that a value exists.

    Performance

    Secret scanning on every output would be expensive if done naively. We use several optimizations to keep latency under control. Short strings below a minimum length are skipped entirely. Pattern matching uses compiled regex with early-exit semantics. And for large outputs, we scan only the delta — the new content that wasn't present in the previous action.

    The result is sub-millisecond overhead for typical actions, and under 5 milliseconds even for large file writes.

    Configuring the Engine

    You can tune the redaction engine through your SafeClaw policy file. Whitelist specific patterns, adjust entropy thresholds, or disable scanning for certain directories (like test fixtures). Full configuration details are in our documentation.

    The source code is available on GitHub — we believe security tools should be auditable.

    Why This Matters

    Secrets redaction isn't glamorous. It doesn't make the agent smarter or faster. But it prevents the kind of incident that makes headlines — and more importantly, it lets you deploy AI agents with confidence, knowing that your most sensitive data has a guardrail around it.