Building Secure Autonomous Infrastructure

Stratus Labs is an applied AI research lab building autonomous security infrastructure for regulated software systems.

Our thesis is that as software systems grow more complex and increasingly autonomous, security failures will not come from a lack of scanning tools or compliance frameworks, but from the absence of continuously operating systems that can understand and act inside production environments.

Security is not intelligence constrained.
Rather, it is environment constrained.

Why is this Possible?

Point solutions do not scale. Modern security teams rely on a fragmented collection of scanners, CI checks, pentest reports, and compliance tools. These systems generate findings and documentation, but they defer remediation to humans. As infrastructure changes continuously, this model breaks down.

Regulated environments require local control. Healthcare, finance, defense, and government systems cannot depend on opaque, cloud hosted automation. Security systems must operate inside the environment, respect access boundaries, and produce deterministic, auditable outcomes.

Security work is environment constrained. Most real vulnerabilities emerge from the interaction between code, infrastructure, identity, and deployment. We believe that automating meaningful security work is primarily constrained by environment access and persistent system context rather than by new breakthroughs in model intelligence.

Our Approach

We build autonomous security agents that live directly inside production environments.

Stratus is deployed as a local first CLI that connects to a company's repositories, infrastructure, and access controls. From there, agents continuously analyze the system, simulate real attack and failure modes, and execute remediation through pull requests and configuration changes across code, infrastructure as code, identity policies, and deployment stages.

Every action is logged with full context and scoped to confiugurable permissions across your environments, and produces audit-ready artifacts and reports for compliance review.

Over time, agents improve through local, outcome driven reinforcement learning performed entirely within each environment. No customer data is centralized or exported.

Guiding Principles

Bias toward system native agents. Security automation must operate in the same environments humans secure today including codebases, infrastructure, identity systems, and deployment workflows.

Auditability as a first class output. Compliance artifacts are valuable. Every action must be logged, reviewable, and suitable for regulated audit and review.

Local first by default. Agents should run where the software runs with scoped permissions and strict data isolation.

Looking Forward

We see Stratus as foundational infrastructure for how regulated software systems are secured.

As software becomes more autonomous, security systems must do the same.

If you're interested in building or deploying local-first autonomous security systems inside regulated environments, we'd love to talk.