No one intended to assess AI-created code at 3 a.m. But with the growth of AI-assisted software development, DevSecOps teams are managing increasing amounts of code, dependencies, and security requirements. AI makes delivery faster, but that doesn’t make the actual process of application security assessment pointless.
Why Is AI Important to DevSecOps?
At present, AI aids in performing numerous steps of the software development process. Developers utilize coding tools to write functions, interpret code, create tests, and suggest corrections. Security groups use AI to prioritize vulnerabilities, detect threats, analyze code, and respond to incidents.
That said, a quicker development process can lead to governance issues. According to GitLab’s 2026 AI Accountability Report, based on a global survey of 1,528 developers and technology buyers, there is a pressing need for context, traceability, and accountability in AI-driven DevSecOps.
In light of that, generative AI in DevSecOps becomes much more than a discussion regarding productivity. Organizations need to know where the AI-generated code came from, what it is supposed to do, and who is responsible for reviewing the code before it goes live.
What AI security risks look like
The security of AI-generated code is a concern. Veracode’s 2026 report showed that roughly 44% of AI code-generation jobs resulted in code containing a known security vulnerability. The security pass rate averaged 56%, but it varied by vulnerability type and model.
Thus, AI-generated code should not be considered less scrutinized than human-written code. It has to comply with secure coding standards, testing protocols, and meet the same approval requirements as any other code.
Is AI Causing a Supply Chain Issue?
AI has risks through suggested dependencies. A programming assistant may suggest an incorrect, outdated, vulnerable, or non-existent dependency. Attackers can register plausible package names that AI tools hallucinate or recommend, then publish malicious code under their names. This is called slopsquatting.
To help keep AI software supply chain security, teams need to verify any AI-suggested dependencies before using them. Techniques such as package allowlisting, software composition analysis, and vulnerability monitoring can help secure the software supply chain.
Classic DevSecOps vs AI-Powered DevSecOps
| Field | Classic methodology | AI-augmented methodology |
| Code verification | Human-reviewed code supported by automated testing | AI-assisted code testing combined with automated AI security testing and human review |
| Safety testing | Security testing performed throughout the CI/CD pipeline | Security testing integrated into CI/CD pipelines |
| Dependencies | Dependencies scanned for known vulnerabilities | Packages verified for vulnerabilities, authenticity, and provenance |
| Governance | Developer and security responsibility | AI use, approvals, responsibility, and code origin are recorded |
Conclusion
The DevSecOps best practices 2026 include the use of AI tools for application security together with strict programming practices, automatic systems of control, verification of dependencies, and human checks.
If you want to learn the art of using AI in DevSecOps, you can take advantage of the DevSecOps training courses offered by Spoclearn. They can help you improve existing DevSecOps practices and integrate them into AI-powered software development.
Frequently Asked Questions
1. Is AI-produced code automatically safe?
Veracode research in 2026 found that roughly 44% of the AI code-generation tasks tested produced code containing a known security vulnerability. AI-generated code will need to be tested and analyzed before approval.
2. What is slopsquatting?
Slopsquatting is an attack where attackers register plausible but non-existent package names which AI tools could mistakenly suggest to users. Teams using these packages may be exposing their applications to malicious code injection.
3. Will AI security tools replace human reviewers?
Not completely. AI security tools may be able to recognize patterns, rank issues, and perform routine checks automatically. But one still needs a human reviewer to ensure that the business logic, security context, architecture, and code behavior are appropriate.