BUSINESS surveillance for AI-generated code while embedding automated detection throughout the CI / CD pipeline. The more your teams rely on AI, the more essential this routine testing is at every stage of development.
CISOs should also establish clear policies for AI tool evaluation and approval. No team should be using an AI-based tool – even for‘ minor’ tasks – unless it has gone through a formal risk assessment, with inputs from your security, legal, data governance and compliance teams. At a minimum, organisations must assess whether the tool protects sensitive data according to their security policies and verify that its decision-making process can be explained and audited when necessary. Beyond basic security, there must be clear processes for disclosing, tracking and patching any vulnerabilities that emerge, while ensuring the tool provides comprehensive logs, version control and full traceability of all actions. Most critically, you must establish clear protocols for identifying and protecting your proprietary code, trade secrets and sensitive business information.
To effectively address the challenges posed by AIgenerated code, organisations need to completely overhaul their code review processes. This means upgrading your tools and practices to:
• Detect AI-generated patterns with enhanced static analysis.
• Run vulnerability scans tailored for LLMgenerated code.
• Pair junior developers with experienced reviewers on AI-heavy projects.
• Monitor model behaviour, output anomalies and usage drift.
• Use AI agents to audit pipelines and uncover hidden risks.
The goal isn’ t to slow development, but to ensure full visibility into the code being written, its connections and its potential risks.
Security starts with how developers use AI from the very first prompt. This is why training matters. Developers must learn what secure prompting looks like, when to escalate concerns, and how to spot AI-generated code that appears correct but is ultimately flawed. CISOs should prioritise supporting junior developers with security-focused tools, contextual guardrails and senior mentorship to identify AI coding errors. Given that our traditional instincts for spotting errors tend to fail with AI-generated code, implementing mandatory review checkpoints for all critical AI-assisted outputs becomes necessary.
Ignorance is not bliss
At a minimum, organisations must assess whether the tool protects sensitive data according to their security policies and verify that its decision-making process can be explained and audited when necessary.
Organisations that build security into AI development from the start will ship faster, protect their core assets and maintain control as complexity grows.
The difference in cost between prevention and incident response multiplies with every new deployment. A strong CI / CD foundation, clear policies and developers trained to understand and manage AI risks are essential to scaling safely.
CISOs who delay putting the basics in place will find themselves managing crisis after crisis, as preventable vulnerabilities multiply throughout the codebase. CISOs who act now, enacting secure AI practices enabled by a strong DevOps framework, will build customer trust and maintain regulatory compliance. Security is not a barrier to AI adoption; it’ s the foundation that makes AI adoption sustainable at enterprise scale.
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