Use case · Coding agents
AI coding tools put the full codebase in front of a model. Point them at Raidu’s endpoint and checks run on every prompt, every tool call and every output before the model sees them, and every token is counted against a per-developer budget.
Runs inside your AWS, GCP or Kubernetes install.
Last reviewed
Illustrative. No customer data.
The difference
Point your coding tools at Raidu's endpoint. PII, secrets and exfiltration attempts are checked at every step, not after the fact.
Source: Claude Code hook integration and session audit service
How Raidu handles it
Before the prompt reaches the model, Raidu scans it for dozens of personal-data and credential types. Anthropic, OpenAI, GitHub, Slack, Google and AWS keys, private keys, connection strings, JWTs and bearer tokens are blocked by name. The developer sees which type was caught.
Before any tool executes, Raidu checks the call. Reads of .env, credentials, .pem, .key, .aws, .ssh and .kube are refused. Exfiltration patterns (curl -d, wget --post, scp, rsync, nc) are blocked. The tool never runs; the refusal is logged.
Tool output is scanned for personal data before the agent reads it. At session end, the full session is written to the audit trail: every prompt, every tool call, every decision, every token count.
Products that do it
Dozens of credential types caught by name. Point any coding tool at Raidu's endpoint to govern every session.
Read more ›Each developer gets a key. Every token is metered against a budget, with alerts.
Read more ›61 guardrail policies run on every prompt before the model is called. Jailbreak, prompt injection and encoding attacks are included.
Read more ›Every session is logged from prompt to stop. Tamper-evident, write-once records.
Read more ›The product
Product windows from the films, with sample data. No customer data.
The record
Every coding session produces a tamper-evident audit trail from the first prompt to the last step.
Common questions