Secure Coding for Software Engineers - Secure Code Warrior

Secure Code Warrior Learning builds the secure coding capability developers need at every stage of the AI transition - from human-written code to AI-assisted development to fully agentic systems. Rather than annual compliance training, it detects signals from real developer activity - the languages they code in, the AI tools they use, the vulnerabilities they introduce - and triggers targeted, hands-on learning in near-real-time. 

Developers practice through Quests, coding labs, challenges, and tournaments across 75+ languages and frameworks, including 1,000+ dedicated AI/LLM/MCP learning activities, then prove measurable improvement with SCW Trust Score®. Content aligns to OWASP Top 10, NIST, PCI DSS, CRA, and NIS2. Built on 11 years of secure coding leadership, SCW Learning has helped organisations reduce introduced vulnerabilities by up to 53% and achieve 3x faster mean time to remediate.

Learning Objectives

• Validate AI-generated code — recognise insecure patterns produced by LLMs and correct them before commit.
• Apply secure coding fundamentalsacross 75+ languages and frameworks, mapped to 650+ real-world vulnerability topics.
• Manage the new AI attack surface — prompt injection, insecure AI outputs, agent misuse, credential exposure, and data leakage.
• Govern AI agents and their protocols — permissions, identities, and trust boundaries across MCP, A2A, and ACP.
• Define security intent, not just implementation — write security requirements and assess AI-generated architectures.
• Meet policy and regulatory expectations — align to OWASP Top 10, NIST, PCI DSS, CRA, and NIS2 with audit-ready evidence.
• Demonstrate measurable improvement— track proficiency and behaviour change through SCW Trust Score®, not course completions.

Target Audience

Everyone accountable for code that ships — whoever, or whatever, wrote it.

Practitioners

• Software engineers and developers using AI assistants in daily production work
• Code reviewers and tech leads approving AI-generated contributions
• Security champions embedded in engineering teams
• Architects defining security requirements for AI-assisted delivery
• QA, DevOps, and platform engineers working across pipelines and repositories

Leaders responsible for secure development

• Security and AI governance leaders — establish visibility, contributor accountability, and demonstrable developer competency.
• AppSec leaders — scale developer-driven security and reduce introduced vulnerabilities without adding review headcount.
• Engineering leaders — keep velocity while cutting rework and shipping resilient code.
• Learning and development leaders — run structured, measurable programs that prove impact and satisfy compliance requirements.

No prior security specialisation is required. Programs are role-based and risk-aligned, so a Stage 1 team building fundamentals and a Stage 5 team directing coding agents each get the content that fits their reality.

Learning Approach

Practice, not passive content. Behaviour change, not completions.

Traditional security training measures completion. Static scanning finds problems after they're introduced. Our approach improves developer behaviour before code reaches commit — the foundation of effective AI software governance.

A five-step cycle

1. Assess — benchmark secure coding proficiency across languages and vulnerability categories.
2. Assign — deliver targeted, risk-based learning paths through Quests, aligned to role, language, and real vulnerability data.
3. Practice — reinforce skills in hands-on coding labs, challenges, walkthroughs, and tournaments inside live coding environments with immediate feedback.
4. Validate — measure improvement objectively with assessments and SCW Trust Score®.
5. Improve — reduce recurring vulnerabilities over time, with evidence.

What makes it work

• Adaptive learning — training is assigned automatically from real signals: commit risk, vulnerability findings, capability gaps, and AI tool usage. No manual assessment cycles, no one-size-fits-all curriculum.
• In the flow of work — IDE, repository, and pipeline integrations put learning where developers already are.
• Stage-aware content — 1,000+ dedicated AI, LLM, and MCP learning activities across 11,000+ total activities, mapped to the eight stages of AI adoption.
• Closed loop with governance — SCW Trust Agent observes AI's contribution at commit level and triggers learning when risk signals spike, completing the Observe → Measure → Act → Prove loop.