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Policy

Security at Lineman

How we protect your data, your account, and your code.

The Lineman team

Lineman handles code and tool output for engineering teams, so security isn't a page we refresh once a year. It's part of the product. Your code passes through our service to be compressed, so how we protect that data, your account, and our infrastructure matters as much as the token savings do.

This post is a high-level look at how we approach security. The living detail, including our independent audit and the certifications we're working towards, is on the Security page.

Independently assessed

We don't just grade our own homework. Lineman is continuously assessed by an independent external security platform that covers code scanning, dependency vulnerabilities, secrets detection, cloud posture, and container image analysis across our production infrastructure. Our audit report is published and can be requested directly from the Security page.

Working towards formal certification

Our internal security programme is built around recognised information-security frameworks, and we're actively engaged with auditors to obtain formal attestation against each one. We'll update the Security page as each certification is awarded. Until then we describe them as in progress rather than claiming a status we haven't earned.

Protecting your code

The strongest data protection is not keeping data you don't need:

  • Transient processing. Your code is processed in real time and discarded after the response is delivered, so there's no persistent store of your files to breach.
  • Never used for training. Your content is never used to train or fine-tune models.
  • Separation of concerns. Operational usage data (task types, token counts, latency) is anonymised and stored separately from any code you process.

The data-handling commitments behind these points are set out in our Privacy Policy.

Protecting your account

Around your account we maintain authentication controls and keep security logs: login events, IP addresses, API access patterns, and anomaly-detection signals. They're there to detect and respond to suspicious activity, and they protect you and the wider service. The Privacy Policy describes what we keep.

A continuous posture

Security at Lineman is ongoing work, not a finish line. We run continuous external assessment, monitor for anomalies, and treat incident response as a first-class part of operating the service. As the programme matures we'll keep the Security page current, so you can always see where things stand.

If you have a security question, a disclosure, or want to request our audit report, get in touch via contact. We take reports seriously and respond quickly.

Related

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Your code is yours

Lineman processes tool output to compress it, then moves on. We don't train on your code. A plain-English account of how your data is handled.

Policy

Trust, data protection, and compliance

Who we are, the rules we operate under, and the commitments we make about your data: UK GDPR and the company behind Lineman.

Product

How to Build an LLM Spend Audit by Department in 7 Steps (2026)

Quick Guide: How to Build an LLM Spend Audit by Department in 7 Steps Define your cost attribution dimensions — Decide whether you'll track by department, team, repository, or individual developer. Set up token telemetry collection — Install an SDK wrapper or proxy that captures every LLM API call with metadata. Configure department tagging — Map each request to a department using git context, API keys, or manual labels. Aggregate costs by time window — Build hourly, daily, and monthly rollups so you can spot trends and spikes. Add prompt compression middleware — Use Lineman to cut token costs by 40%+ while maintaining output quality. Create budget thresholds and alerts — Set per-department limits that notify you before overspending. Export to your FinOps dashboard — Push data to CloudWatch, Prometheus, or your existing observability stack. How to Build a Department-Level LLM Spend Audit 1. Define Your Cost Attribution Dimensions Start by mapping how your organization actually uses LLM APIs. Most engineering teams find that repository-level attribution gives the clearest picture of spend.