Services

PRIMARY PRACTICE

Security Programs

Find it. Fix what matters. Prove it.

We build and run the program: the pipeline that finds the flaw, the triage that ranks it, the policy that keeps it from coming back, and the incident command for the day a flaw gets through.

  1. Know what is broken.

    One pipeline watches code, dependencies, secrets, cloud configuration and clusters. Everything it finds lands in the same queue, and every product is measured the same way, so you can say which one is in worse shape and by how much.

    • SCA, SAST, DAST and secrets, on every push
    • Cloud configuration and Kubernetes, in the same sweep
    • Every product measured on the same scorecard
    • Open source and self-hosted, lower license spend

  2. Fix what matters.

    Agents clear the noise before an engineer ever sees it. The only tickets that reach the team are real and reachable, and every skip is written down. SLAs turn the backlog into dates somebody has to answer for.

    • Engineers only open findings that are real
    • Every skipped finding stays on the record
    • SLAs with a named owner and a date
    • HackerOne reports triaged the same way

  3. Make it hard to break twice.

    The cheapest vulnerability is the one the platform will not let you deploy. We model the threats while the architecture is still a diagram, and then make the dangerous configuration impossible to ship rather than discouraged.

    • Threat models before the architecture is built
    • Dangerous configuration never reaches an environment
    • Guardrails on the cloud accounts themselves
    • Code audits and vulnerability identification, internal and external

  4. Hold the line in production.

    Some of it gets through anyway. We watch the vulnerabilities that matter, keep AI on the alert noise so the team only sees what is real, and put an incident commander on the bridge the day something lands.

    • Critical vulnerabilities tracked through to closure
    • CNAPP across every cloud environment
    • Alerts triaged by AI before they reach you
    • An incident commander runs the response

  5. Secure the AI you ship.

    An LLM feature breaks in places a classic security program never looks. Guardrails, abuse cases and evaluation go into the product before it reaches a user, not after the first incident.

    • Guardrails and abuse cases before launch
    • Prompt injection and tool use boundaries
    • Agent architectures reviewed before they ship

THE OTHER HALF

AI Engineering

We build the agents that run the program.

The program above runs on agents instead of headcount because we build the agents ourselves.

  1. Agents that survive production.

    An agent doing real work fails in ways a demo never shows. Ours run sandboxed and on a budget, with a kill switch that fails closed and enough instrumentation to see what happened afterwards.

    • Agents that run sandboxed and on a budget
    • Evals before features, on non-deterministic systems
    • A kill switch that fails closed
    • Full-stack delivery: web, API, infrastructure

  2. Proof, not a portfolio.

    Six products carry the claims on this page. We designed them, built them, then attacked them ourselves. They exist to show what we can build; what we sell is the program above.

    • Six products designed, built and attacked
    • Every claim on this page has a case