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Service / AI Security & DevSecOps

Ship AI features without shipping AI vulnerabilities

Built for teams shipping AI features in production. Threat modeling, red-teaming, and DevSecOps automation for teams building with LLMs, agents, and ML pipelines — by engineers who've shipped production AI systems and broken them.

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The problem

Your AI roadmap is moving faster than your security model

Every week, your team ships a new LLM feature, a new agent, a new RAG integration. Each one expands your attack surface in ways traditional appsec wasn't built to handle — prompt injection, data exfiltration through tool calls, model abuse, and supply-chain risk in your model and prompt registry.

You don't need a 200-page AI governance binder. You need senior engineers who can red-team your system, fix what they find, and leave your team stronger than they found it. That's what this service is.

Capabilities

What we cover

GenAI threat modeling

Map the attack surface of LLM-powered features end-to-end — prompt injection, data exfiltration, agent misuse, and tool-call abuse — against the OWASP LLM Top 10 and MITRE ATLAS.

AI red-teaming

Adversarial testing of your models, prompts, RAG pipelines, and agentic workflows. Jailbreaks, indirect injection, supply-chain probes, and abuse-case discovery before your users find them.

ML / MLOps security

Protect training pipelines, model registries, feature stores, and inference endpoints. Model poisoning defense, dataset provenance, and access controls for the full ML lifecycle.

DevSecOps automation

Shift left with SAST, SCA, IaC scanning, and secrets detection wired directly into your CI/CD. Policy-as-code, signed artifacts, and a paved road developers actually want to use.

Our approach

A four-step process, designed to ship

  1. 01

    Discovery & scoping

    Two weeks. We map your AI systems, data flows, and existing controls. You get a written threat model and prioritized risk register tied to business impact.

  2. 02

    Adversarial assessment

    We red-team your models, prompts, and pipelines using the same techniques attackers use. Findings are reproducible, severity-rated, and mapped to OWASP LLM and MITRE ATLAS.

  3. 03

    Remediation & hardening

    We embed with your engineers to fix what we found — guardrails, input/output validation, monitoring, and CI/CD controls. Code, not slideware.

  4. 04

    Continuous assurance

    Optional ongoing engagement: regression testing, monthly red-team exercises, and a security partner your team can ping in Slack.

Deliverables & outcomes

What you walk away with

Deliverables

  • GenAI threat model & risk register
  • Red-team report with reproducible findings
  • Remediation roadmap with severity ranking
  • Hardened reference architecture
  • CI/CD security pipeline configuration
  • Runbooks for AI incident response

Outcomes

  • Ship LLM features without blocking on security review
  • Pass enterprise & investor AI security due diligence
  • Reduce time-to-fix for AI vulnerabilities by 60%+
  • Demonstrable alignment with OWASP LLM Top 10 & NIST AI RMF
  • Engineers who actually understand AI threat models
  • Audit-ready evidence for SOC 2, ISO 42001, and EU AI Act

Engagement types

Three ways to work with us

Point assessment

2–4 weeks. Threat model + red-team of a specific AI feature or product surface. Best for pre-launch reviews and investor due diligence.

Embedded program

3–6 months. Senior engineers embedded with your team to design, harden, and ship a production-grade AI security program.

Continuous partner

Ongoing retainer. Monthly red-teaming, on-call advisory, and a security partner who knows your stack as well as your engineers do.

FAQ

Common questions

We use a hosted LLM provider (OpenAI, Anthropic, Google). Do we still need AI security?
Yes. The model provider secures their infrastructure; you secure how you use their model. Prompt injection, data leakage through prompts, insecure tool/plugin design, and inadequate output filtering are all your responsibility. Hosted models don't fix them.
How is this different from regular application security?
Traditional AppSec assumes deterministic code. AI applications are probabilistic and influenced by untrusted inputs in ways that traditional tools don't model. The OWASP Top 10 for LLM Applications exists because the threat model is genuinely different. We bridge both worlds.
Do you work with on-prem and self-hosted models too?
Yes. Self-hosted, fine-tuned, and open-weight models add their own threat surface — model file tampering, supply chain attacks via Hugging Face, GPU runtime vulnerabilities. We cover them.
What size of company is this for?
We work with companies from 20 employees to 500. Below that, we recommend starting with our vCISO offering. Above that, we typically partner with your in-house security team rather than replace it.
Can you sign an NDA before we share details?
Yes — always. Mutual NDA before any technical discussion.

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