Secure Code Warrior • Application security
Secure Code Warrior Trust Agent is the industry-first AI traceability and governance product that makes AI influence in software development visible, attributable, and enforceable at the point of commit. Trust Agent records which LLMs influenced specific commits, evaluates models against Secure Code Warrior's security benchmarks, tracks Model Context Protocol (MCP) servers installed by AI agents, and enforces policy at pull-request time.
Merito sells Secure Code Warrior Trust Agent and operates the commit-level observability integration, LLM benchmarking configuration, MCP discovery rollout, and Trust Score correlation that turn Trust Agent into a working AI governance layer.
What it is
Secure Code Warrior Trust Agent makes AI influence in software development visible, attributable, and enforceable at the point of commit. The product records a verifiable record of which LLMs (sanctioned and Shadow AI models) influenced specific commits, supporting governance and audit requirements without storing source code or prompts. The architecture addresses regulatory concerns about sensitive data exposure that block commit-level traceability tools that store full source.
LLM security benchmarking leverages Secure Code Warrior's benchmark data to evaluate AI models and enforce approved AI usage policies based on measurable security performance. MCP discovery tracks which Model Context Protocol servers are installed and active, preventing AI agents from accessing sensitive internal tools or databases through unvetted or risky connections. Trust Score integration correlates developers' skill sets with their AI usage and vulnerability benchmarks to identify the risk level and enforce policy before code reaches production.
Trust Agent reached general availability in March 2026 after a beta program launched September 24, 2025. The product is new and the integration ecosystem is less developed than the longer-running Learning surface. Programs adopting Trust Agent should plan for early-stage maturity and evolving capability depth. The architecture is sound, and the commit-level traceability addresses a real governance gap that more mature AppSec products do not solve.
Ideal use cases
What it is best at
Records which LLMs (sanctioned and Shadow AI) influenced specific commits without storing source code or prompts. The architecture addresses regulatory concerns that block commit-level traceability tools that store full source.
Secure Code Warrior's LLM security benchmark data evaluates AI models against measurable security performance. Programs enforce approved AI usage policies based on benchmark results.
Tracks which Model Context Protocol servers are installed and active. Prevents AI agents from accessing sensitive internal tools or databases through unvetted or risky connections.
Correlates developer Trust Score (secure-coding skill measurement) with AI tool usage patterns and vulnerability benchmarks. Programs identify which developers introduce risk and enforce policy before code reaches production.
Core capabilities
What Trust Agent observes about AI influence on the codebase.
Commit-level LLM tracking
Verifiable record of which LLMs influenced specific commits, supporting governance and audit requirements.
Sanctioned and Shadow AI coverage
Tracks both approved AI tools and Shadow AI models that developers may use without security team approval.
Output analysis
Surveys how much code is AI-generated to give programs the actual AI adoption data behind their codebase.
Source-and-prompt-free recording
Records influence without storing source code or prompts. Addresses regulatory concerns about sensitive data exposure.
How Trust Agent evaluates AI models.
Benchmark dataset
Secure Code Warrior's LLM security benchmark data evaluates AI models against measurable security performance.
AI usage policy enforcement
Programs author policies that approve or restrict AI tool usage based on benchmark results.
Per-LLM performance tracking
Track AI model security performance over time as new models and versions appear.
Tracking AI agent connections to internal systems.
MCP server inventory
Tracks which Model Context Protocol servers are installed and active across developer environments.
Unvetted connection prevention
Identifies and prevents AI agents from accessing sensitive internal tools or databases through unvetted or risky MCP connections.
MCP policy controls
Author policies that govern which MCP connections are allowed for which developer cohorts.
How Trust Agent ties AI governance to developer skill.
Trust Score correlation
Correlates developer Trust Score with AI tool usage and vulnerability benchmarks.
PR-time policy controls
Log, warn, or block pull requests based on AI usage policies and Trust Score thresholds.
Risk identification
Identifies which developers introduce risk based on AI usage patterns and skill measurement.
Where it fits in the stack
Deployment and implementation
Licensing and packaging
Secure Code Warrior Trust Agent
Industry-first AI traceability product. Commit-level AI usage visibility, LLM security benchmarking, MCP discovery, Trust Score correlation, and PR-time policy controls.
Best for: Engineering organizations with significant AI code-assistant adoption needing commit-level governance evidence.
Merito services
Merito sells licenses and the delivery work around them. Pick the service that matches where you are in the lifecycle.
Commit-level observability integration, LLM benchmarking configuration, MCP discovery rollout, and Trust Score correlation setup.
Explore service02Trust Agent integration into developer workflows across major SCMs and AI code-assistant tools.
Explore service03Developer-facing AI code-assistant adoption discipline and MCP server hygiene.
Explore service04Named engineer, priority SLAs, and release-window coverage for Trust Agent in production.
Explore service05Long-term run support including ongoing LLM benchmark calibration, MCP discovery maintenance, and Trust Score correlation operations.
Explore service06Role-based training for CISOs, AppSec leaders, and security operations teams using Trust Agent output.
Explore serviceTrust Agent licensing
Industry-first AI traceability is integration work, not just a product purchase. Buy Trust Agent through Merito and get the commit-level integration, LLM benchmarking, MCP discovery rollout, and Trust Score correlation together.
Merito point of view
Commit-level AI traceability is genuinely a new category. CISOs and audit leaders need to demonstrate which AI tools influenced production code, and Trust Agent records that influence at the point of commit without storing source code or prompts. The architecture addresses regulatory concerns about sensitive data exposure that block competing tools that store full source.
Trust Agent is new (GA March 2026 after September 2025 beta). The integration ecosystem is less developed than longer-running AppSec products. Programs adopting Trust Agent should plan for evolving capability depth and treat early adoption as part of the value (influence on roadmap, support attention) rather than expecting full feature breadth from day one.
Programs without significant AI code-assistant adoption get less value from Trust Agent. The architecture pays back when Copilot, Claude Code, Cursor, or other AI tools are in widespread daily use. Programs in pilot phases or with limited AI tool adoption should sequence other AppSec products first and add Trust Agent once AI code-assistant usage becomes meaningful.
What buyers usually underestimate
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Frequently Asked Questions
Consultation request
Share your AI code-assistant adoption posture, MCP server inventory, and audit requirements. A Merito Secure Code Warrior specialist follows up within one business day.
Industry-first traceability
Records which LLMs influenced specific commits without storing source code or prompts.
MCP discovery
Tracks Model Context Protocol servers installed by AI agents to prevent unvetted internal tool access.
Next step
A Trust Agent engagement with Merito starts with the AI tool inventory and commit-level observability integration. Programs with significant AI code-assistant adoption see the most direct value.