AI-powered development tools are rapidly becoming part of everyday engineering workflows. Developers are increasingly relying on large language models (LLMs) and AI assistants to generate boilerplate code, debug issues or accelerate feature development. The technology is improving productivity, but it is also introducing new, concerning security risks that teams need to be aware of.
Insecure patterns, vulnerable dependencies and logic errors are just some of the issues with AI-generated code. Traditional manual reviews are struggling to keep up, leading to problems being overlooked in high-velocity environments. Many development teams are implementing specialised platforms that provide automated protection to address these complex risks.
The importance of securing AI-generated code
AI coding tools have sped up development at an incredible pace. However, 45% of AI-generated code has security flaws. These must be managed and addressed carefully and proactively to ensure they don’t become problems. AI models are trained on large datasets of public code, which can lead them to emulate outdated practices or even reproduce insecure coding patterns.
Common security concerns include:
- Hallucinated functions.
- Vulnerable open-source dependencies.
- Exposed credentials or secrets.
- Insecure code patterns.
- Limited visibility of generated logic.
The challenges can undermine the efforts of large-scale companies that rely heavily on automation. One report found that 42% of code is now AI-assisted, a figure expected to rise to 65% by 2027. It is therefore imperative that AI-generated code is secured by identifying and fixing security risks before it becomes part of a live application.
How platforms help secure AI-generated code
The days when engineering leaders treated secure code review as a separate stage to be conducted later are long gone. Thankfully, there are platforms that help development teams identify problems with AI-generated code early, letting them respond to issues proactively. Such platforms integrate automated security checks directly into development workflows, analysing code, dependencies and repository activity spot and flag potential vulnerabilities.
Platforms can use dependency scanning, secret detection and static application security testing (SAST) to flag potential security issues as early in the development life cycle as possible. Integration with IDEs, CI/CD pipelines and pull requests lets development teams review both AI-generated code and human code automatically. This ensures companies keep strong security practices without sacrificing the benefits of AI-assisted development, particularly speed.
The best platforms for securing AI-generated code
Less than half of organisations have an AI governance policy. Development teams must ensure they have policies to address the risks AI code poses. Numerous platforms can help development teams secure their AI-generated code, but the names on this list stand out as the best.
1. Legit
Legit is an application security posture management (ASPM) platform designed to help large enterprises identify and manage security issues in their development environments. Legit’s VibeGuard secures AI-generated code, agents and workflow, making it more comprehensive than a single tool. VibeGuard is integrated directly into an integrated development environment (IDE) and connects to a centralised management console so it can secure AI code as soon as it’s created.
Legit offers enterprise-level security workflow automation and secrets scanning, as well as embedded security tools and compliance and governance reporting modules. All of this combines to make Legit a standout choice for securing AI-generated code.
Key features
- Enterprise-level security workflow automation
- Embedded security tools
- Compliance and governance reporting modules
2. Veracode
Veracode‘s application risk management platform aims to find risks in the entire software development life cycle.
Veracode’s AI-powered engine scans code to find and resolve risks, moving beyond simple detection by rooting out the cause of vulnerabilities at their core and producing analysis that helps teams remove threats.
Key features
- Static analysis that enhances AI-generated code security
- Can identify and fix flaws in over 100 languages and frameworks
- Uses SCA and Package Firewalls to safeguard software supply chains
3. SonarQube
SonarQube secures first-party, third-party and AI-generated code with advanced SAST, secrets detection and infrastructure as code (IaC) scanning. Its security compliance and regulatory reports track against common standards, covering more than 30 languages and frameworks with over 6,000 rules.
SonarQube also stands out for having multiple pricing choices on its website, with Free, Team and Enterprise options available.
Key features
- Covers over 30 languages and frameworks with over 6,000 rules
- Secures first-party, third-party and AI-generated code
- Multiple pricing options, including a free version
4. Semgrep
Semgrep is powered by security that learns as teams build, helping it prioritise reachable vulnerabilities and eliminate false positives. It aims to deliver fixes without disrupting workflow by securing code with built-in guardrails that guide safe fixes for code before it ships.
Semgrep can detect complicated issues like broken authorisation, IDORs and flawed multistep logic. It combines AI reasoning with deterministic static analysis to understand structure, naming and developer intent.
Key features
- Can detect complex issues
- Deterministic static analysis
- Fixes issues in PRs, IDEs, CI or AI tools
Criteria to determine the best platforms for securing AI-generated code
The platforms chosen for this list were handpicked based on several factors. Those that integrate directly into developer workflows were favored, as were those that feature automated detection of security issues in source code. Platforms that support enterprise security governance were highlighted to ensure companies can manage risk in large engineering teams.
Other important considerations were platforms that can detect vulnerabilities in both human and AI-generated code, secret detection and dependency scanning. These abilities help teams uncover issues early in the development life cycle and maintain consistent security practices.
Choosing the right platform for AI-generated code security
AI coding assistants are becoming an increasingly core part of development workflows. Companies must react and adapt accordingly to ensure they have the proper security practices in place to secure AI-generated code and proactively address the new and evolving risks it poses.
Platforms that can integrate security functions directly into development teams’ workflows can help ensure both their human and AI-generated code is secure without sacrificing the speed and optimisation that AI coding assistants provide.