For CISOs & Security Leaders
AI is writing code faster than you can review it
Last verified: June 2026· Security Leaders engagement
Agent-generated code, prompts carrying sensitive data, and new injection surfaces are entering your repos daily. We help you put policy, scanning, and guardrails around all of it — without slowing the team down.
Compliance plus AI gets budget in any environment. We build the controls that let your engineers move fast with agents while keeping security and data governance provably intact.
What’s on your desk
Unreviewed AI-generated code in production
Volume is up and review capacity isn’t. Hallucinated APIs, missing tests, and subtle vulnerabilities slip through. You need automated, AI-aware scanning in CI.
Sensitive data leaking into prompts
Engineers paste secrets, PII, and proprietary data into prompts and tools. Without controls, your data is leaving the building one request at a time.
New prompt-injection attack surface
Agents that read external content and call tools introduce injection vectors traditional AppSec doesn’t cover. We map and mitigate them.
Data residency closes the easy path
For regulated workloads, hosted APIs aren’t an option. We stand up self-hosted inference so nothing sensitive leaves your environment.
How we’d help
The engagements that fit a Security Leaders best — each ships working software and a measurable result.
Production AI Eval Infrastructure
Most teams shipped AI features with zero evals. We build eval harnesses, regression suites, online quality monitoring, and A/B infra for prompts and models.
An eval platform wired into your CI/CD
MCP Server Builds for Internal Tooling
We expose your internal systems — Datadog, Linear, databases, deploy tooling — to AI agents over MCP, so your devs can work against company infra safely.
Internal MCP servers with auth, access control, audit logs
Self-Hosted LLM Infrastructure
For data-sensitive teams (healthcare, finance, defense, EU): local inference, RAG pipelines, and fine-tuning workflows on infrastructure you control.
A working private AI stack on your cloud or on-prem
Agentic Codebase Readiness Audit
We map your codebase against what actually makes it AI-coding-ready — module boundaries, test coverage, type strictness, docs, CLAUDE.md / rules files, and MCP potential — and quantify how far off you are.
A scored report + prioritized remediation roadmap
The proof is open source
We deploy the same infrastructure we build in the open.
Put guardrails around AI in your codebase
We’ll scope an audit of how AI-generated code and prompts flow through your org, then implement policy and scanning where it counts.
The surfaces that are genuinely new
Much of what is said about AI code security is a restatement of ordinary application security with new vocabulary, and it is worth separating the genuinely new surfaces from the familiar ones so that attention goes where it is needed. Insecure generated code is mostly a familiar problem: your existing scanners, review process and CI gates apply, and the change is one of volume rather than kind. What is new is that the volume can outpace review, which is a process risk rather than a vulnerability class.
Dependency provenance is new in a specific way. A model can suggest a package that does not exist, and the risk is not the broken build — a failing build is the good outcome — but that the suggested name is plausible enough for someone else to register. The control has to operate on whether a dependency existed and was trusted before it was suggested, which is a supply-chain question your license scanner, which reads manifests, does not answer.
The prompt data path is new and is usually nobody's responsibility. Source code, customer records and occasionally credentials leave through prompts to model providers, and then through traces into observability stores that are far more widely readable inside most companies than the databases the data came from. Treating this as a gateway problem — one enforcement point for redaction, allowed destinations and retention — is considerably more effective than a policy asking engineers to be careful, because the failure is inadvertent rather than deliberate.
Indirect prompt injection is the one we would put at the top of a list. As soon as an agent can both read external content and call tools, any content it reads is potentially instructions, and there is no complete technical fix available today. The controls that work are architectural: least privilege on every tool, human approval on anything irreversible, treating retrieved content as untrusted input rather than context, and keeping the blast radius of a compromised turn small enough to survive. That is a design constraint on how agents are deployed, which means security needs to be in the room when the tooling is chosen rather than when it is reviewed.