Ship AI and software faster—without losing control
TrueFocus brings AI engineering and DevSecOps together. We help teams secure hybrid software supply chains, govern AI agents, and automate remediation with controls your security team can trust.
Built for complex, regulated environments
Cloud + on-premise
Open-source + enterprise support
Policy-as-code guardrails
Human-controlled automation
The operating problem
AI changes what your delivery controls must protect
Traditional pipelines assume deterministic software. AI agents choose tools, handle sensitive context, and produce variable outputs. Meanwhile, security teams are already managing dependency risk, fragmented hybrid infrastructure, and alert overload.
01
Unknown AI exposure
Teams lack a reliable inventory of agents, models, prompts, plugins, data paths, and tool permissions.
02
Security without context
Scanners generate more findings than teams can resolve, while the risks with real reachability and blast radius wait in the same queue.
03
Automation without boundaries
AI can accelerate fixes, but production access, data residency, and approval rights still need deterministic enforcement.
AI + DevSecOps services
Focused engagements that make intelligent delivery systems observable, governable, and safe to operate across hybrid environments.
Find the controls that matter first
Map your software and AI delivery estate, identify high-consequence gaps, and leave with a sequenced investment plan tied to business risk.
- AI asset and tool inventory
- Hybrid pipeline and policy review
- Prioritized governance roadmap
Secure the software supply chain everywhere
Apply consistent dependency, artifact, identity, and infrastructure controls across cloud and on-premise delivery paths without forcing a platform rewrite.
- Context-aware risk prioritization
- SBOM and artifact governance
- OPA policy enforced locally
Put AI agents through a real delivery lifecycle
Catalog agents, test probabilistic behavior, scope tool access, and enforce promotion criteria before an agent reaches sensitive data or production systems.
- Agent identity and ownership
- Security evals and red teaming
- Runtime policy and audit trails
Turn vulnerability findings into governed fixes
Design remediation workflows in which AI can investigate, propose, test, and route patches—while policy controls decide what may proceed automatically.
- Contextual vulnerability triage
- Deterministic test gates
- Risk-based human approval
A governed delivery pattern
Give AI room to work. Keep policy in charge.
We wrap probabilistic AI behavior in deterministic engineering controls. Agents can analyze and propose changes; tests prove the change; policy evaluates identity, environment, data access, and risk; humans approve the exceptions that matter.
- DiscoverInventory assets, dependencies, tools, identities, and data paths.
- EvaluateTest security, quality, reachability, and operational impact.
- EnforceApply OPA and pipeline gates close to the workload.
- ImproveTune policies and automation from production evidence.
How we engage
Start with evidence. Build the controls. Keep improving.
Phase 1 · Assess
Readiness blueprint
A fixed-scope review of architecture, risk, delivery workflows, and governance gaps.
Phase 2 · Implement
Control-plane sprint
Milestone-based delivery of integrations, policies, evaluation gates, and operating playbooks.
Phase 3 · Optimize
Advisory retainer
Ongoing policy updates, alert tuning, governance reviews, and automation expansion.