Secure Code Warrior has introduced Adaptive Learning, a capability designed to help organizations support AI software governance through targeted training based on identified risks. The feature delivers contextual microlearning and tracks outcomes at the code commit level.
Software development is going through its biggest shift ever, from human-written code, to AI-assisted coding, to fully agentic systems with AI writing and revising everything autonomously. It is introducing code churn at an alarming rate. According to Faros’ 2026 AI Engineering Report, the ratio of lines deleted compared to lines added for merged code has increased 861% each quarter amid high AI adoption. Secure Code Warrior’s Adaptive Learning capability helps move risk reduction further upstream to avoid costly expenditures, and delays down the line that can jeopardize enterprises’ efforts to meaningfully and safely advance their AI roadmaps.
Additionally, according to the 2026 Verizon Data Breach Investigations Report, 45% of employees are now regular AI users on corporate devices, up from just 15% the previous year, with 67% accessing AI services through non-corporate accounts. The same report found that source code is the single most common data type being submitted to unauthorized external AI models, creating significant risk of intellectual property exposure.
The downstream consequences are measurable: exploitation of vulnerabilities has overtaken credential abuse as the leading breach method, rising to 31% of initial access vectors, a 55% increase year over year, yet only 26% of critical vulnerabilities were fully remediated in 2025, with median remediation time climbing to 43 days. Secure Code Warrior’s Adaptive Learning capability helps move risk reduction further upstream, protecting the AI roadmaps enterprises are betting on.
The Adaptive Learning feature is the connection between SCW Trust Agent and its entire learning platform, ensuring training stays aligned with real-time developer activity over time. The launch builds on the foundation of SCW Trust Agent: AI, the industry’s first governance solution designed to make AI influence in software development visible, attributable, and enforceable. The Adaptive Learning capability is powered by two core components, AI Signals and Vulnerability Signals:
- Adaptive Learning AI Signals: delivers personalized training at scale, detecting which AI tools each developer is using, down to the lines of code they commit, and automatically triggering targeted learning relevant to their exact activity. As teams move from AI copilots to agentic systems, Adaptive Learning builds the capability developers need to advance confidently at every stage, so enterprises move faster, and more securely.
- Adaptive Learning Vulnerability Signals: Connects your existing security tools directly to developer learning, automatically identifying real vulnerabilities in the repositories developers work in and delivering personalized training relevant to the code they’re building. Adaptive Learning builds the secure coding habits that keep vulnerabilities out of production.
“At every stage, enterprises are trying to achieve three primary objectives: developers and agents must learn to build securely, businesses must govern what AI can and can’t touch in the codebase, and security teams must be able to trace which AI did what, where, and for whom,” said Pieter Danhieux, CEO, Secure Code Warrior. “With SCW’s Adaptive Learning, organizations and developers can swiftly move from understanding risk, to actively reducing it at scale, and with measurable proof at the commit level. This is imperative as developers move from more traditional workflows, to environments where they are orchestrators of autonomous agents.”
Adaptive Learning generates auditable, per-developer evidence of AI security training tied to production code, and supports compliance with the EU AI Act, ISO/IEC 42001, and the NIST AI Risk Management Framework. Additionally, SCW’s Adaptive Learning feature provides:
- AI-driven learning policies: Define when training is triggered based on real-time AI usage signals.
- Dynamic developer targeting: Identify developers interacting with AI tools based on real activity.
- Targeted learning through quests: Deliver relevant training aligned to real development behavior.
- Track progress and outcomes: View tasks assigned, completed, and evaluate how learners performed over time.
- Automatically trigger learning from vulnerability data: Set policies to automatically assign targeted training based on real vulnerabilities in your applications.
- Personalized to developers and code: Map vulnerabilities to repositories and contributors, so learning is tailored to the developers and code driving real risk.
- Flexible data ingestion: Support API imports from Checkmarx, SonarQube, and Parasoft, plus SARIF uploads, with scheduled syncs coming soon.
