Commit-level governance for AI software development

Trust Agent operationalizes AI software governance at the point of commit — correlating AI model usage, developer risk signals, and secure coding policies to prevent introduced vulnerabilities before code reaches production.

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From the #1 secure coding training company

The enforcement gap

AI is writing code. Your security controls still lag behind.

AI-assisted development is now embedded across modern software delivery:

AI coding assistants generating production-ready code
Agent-based workflows operating beyond developer desktops
Cloud-hosted coding bots contributing across repositories
Rapid, multi-language commits at unprecedented velocity

Most security programs still lack enforceable control at the point of commit. Organizations cannot clearly answer:

Which AI models create production code

If AI-assisted code meets security policy

Whether contributors are trained properly

Whether AI usage aligns to governance standards

Traditional scanning detects vulnerabilities after code is merged. Training strengthens developer capability. Neither provides visibility into how code is generated or evaluated before commit.

Trust Agent closes the gap — correlating AI usage, risk signals, and secure coding capability to reduce software risk at the point of commit.

Product overview

The enforcement engine of AI software governance

Trust Agent turns visibility into actionable insight. It correlates commit metadata, AI model usage, MCP activity, and governance thresholds to highlight risk at commit — without slowing development velocity.

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Discover

Identify contributors and AI model activity

Observe

Maintain AI model traceability at commit

Correlate

Connect AI-assisted commits to defined risk thresholds

Flag

Surface policy misalignment in CI through logs and warnings

Improve

Trigger adaptive learning based on commit behavior

Outcomes & Impact

Prevent risk. Prove control. Ship faster.

Trust Agent reduces AI-introduced vulnerabilities, shortens remediation cycles, prioritizes high-risk commits, and strengthens developer accountability across AI-assisted development.

*In progress

Reduction in introduced vulnerabilities

53%+

Faster mean time
to remediate

82%

AI model
traceability at commit

100%

AI-driven coding risk prioritization

At commit

Core capabilities

Operationalize governance at commit

Traditional application security tools detect vulnerabilities after code is written. Trust Agent provides visibility into AI-assisted code at commit — correlating AI usage, developer risk signals, and secure coding capability to identify elevated risk before code reaches production.

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Developer discovery & intelligence

Identify shadow contributors

Continuously identify contributors, tooling usage, commit activity, and verified secure coding competency.

AI tool & model traceability

See where AI influences code

Maintain commit-level visibility into which AI tools, models, and agents contribute across repositories.

LLM security benchmarking

Security-informed model selection

Apply Secure Code Warrior’s LLM security benchmark data to inform approved AI model and usage decisions.

Commit-level risk scoring

See risk with AI-assisted commits

Correlate AI model usage with developer risk signals and secure coding capability to highlight high-risk code contributions.

Adaptive risk remediation

Reduce repeat vulnerabilities

Trigger targeted learning from real commit behavior to close skill gaps and prevent recurring risk.

How it works

Govern AI-assisted development in four steps

Connect & observe

Integrate with repositories and CI systems to capture commit metadata and AI model usage signals.

Trace AI influence

Identify which tools and models contributed to specific commits across projects.

Correlate & score risk

Evaluate AI-assisted commits alongside developer competency and vulnerability benchmarks.

Reinforce & improve

Trigger adaptive remediation when elevated risk patterns are detected.

Who it’s for

Purpose-built for AI software governance

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For AI governance leaders

Operationalize AI governance at commit with model traceability, benchmark-informed guardrails, and clear visibility into AI-driven development risk.

For CISOs

Demonstrate measurable governance over AI-assisted development and reduce enterprise software risk before code reaches production.

For AppSec leaders

Identify and prioritize high-risk commits to reduce recurring vulnerabilities without expanding review capacity.

For engineering leaders

Adopt AI-assisted development with guardrails that protect developer velocity while reducing rework.

Govern AI-driven development before it ships

Trace AI influence. Correlate risk at commit. Enforce control across your software lifecycle.

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Trust Agent FAQs

Commit-level governance for AI-assisted development

Learn how Trust Agent provides commit-level visibility, developer trust scoring, and enforceable AI governance controls.

How does Trust Agent support AI software governance?

Trust Agent is the enforcement engine within the AI software governance platform. It applies commit-level visibility, risk correlation, and policy controls to prevent introduced vulnerabilities before code reaches production.

What is commit-level risk scoring?

Commit-level risk scoring evaluates individual commits — including AI-assisted commits — against defined policy thresholds, vulnerability benchmarks, and AI model usage signals to surface elevated risk before merge.

How do you govern AI-assisted code at commit?

Effective governance at commit requires:

Visibility into AI model usage
Correlation of commit activity with defined risk thresholds
Enforcement of secure coding and AI usage policy
Audit-ready traceability across repositories

Trust Agent brings these together in a unified enforcement layer.

What AI coding environments does Trust Agent support?

Trust Agent supports modern AI-assisted development environments, including AI coding assistants, agent-based IDEs, and CLI-driven workflows.

Supported environments include tools such as GitHub Copilot (including Agent Mode), Claude Code, Cursor, Cline, Roo Code, Gemini CLI, Windsurf, and other AI-enabled development platforms.

At the API layer, Trust Agent supports major LLM providers including OpenAI, Anthropic, Google Vertex AI, Amazon Bedrock, Gemini API, OpenRouter, and other enterprise AI model endpoints.

Model traceability and commit-level risk visibility are applied consistently across supported environments.

Trust Agent is built to evolve alongside the AI development ecosystem as new coding environments and model providers emerge.

How is this different from traditional AppSec tools?

Traditional AppSec tools detect vulnerabilities after code is written. Trust Agent enforces AI usage and secure coding policy at commit — preventing introduced vulnerabilities before they enter production.

Still have questions?

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