Quick Take
Pen-testing doesn’t need to be stuck in an annual cycle. CIOs should start exploring continuous, AI-powered penetration testing as a fresh approach to keeping vulnerabilities in check. Treat it as a pilot opportunity to see where automation and intelligence can extend your team.
Why You Should Care
Attack surfaces aren’t shrinking; they’re expanding. Critical web vulnerabilities reportedly rose by 150% in 2024, fueled by rapid “vibe coding” practices and an uptick in AI-assisted attacks. Yet most organizations still test only when apps go live, and then once a year. That cadence leaves long windows of exposure where attackers can roam freely.
Current methods fall short in different ways. Automated pen-testing scales well but often drowns security teams in false positives. Manual testing gives richer insights and understands business context, but it’s slow and resource-heavy. Neither is designed for the pace of modern software delivery.
AI agents bring a middle ground worth trialing. They combine the reach of automation with reasoning that can flag which issues truly matter. While not a silver bullet, they can spot vulnerabilities earlier in the cycle, helping SMEs and resource-constrained teams get more coverage without stretching budgets. The key is not to replace humans but to give them smarter tools.
What You Should Do Next
- Run a controlled pilot: Choose one non-critical application or microservice and integrate an AI-enabled pen-testing tool directly into its development pipeline.
- Benchmark against current practice: Compare AI findings with your manual pen-test results and automated scanners. Pay attention not only to the number of vulnerabilities detected but also their relevance and accuracy.
- Establish human oversight: Define a review workflow where security analysts validate high-priority findings. This avoids chasing false positives and builds confidence in the tool.
- Track operational impact: Measure how much faster vulnerabilities are discovered and how remediation time changes. Use this data to build a case for, or against, wider adoption.
- Engage stakeholders early: Share pilot results with developers, risk managers, and compliance officers to align expectations and avoid siloed adoption.
Get Started
- Identify a non-critical app as your test case.
- Benchmark AI-driven results against manual and automated scans.
- Engage your security team early to refine workflows.
- Use the pilot to shape realistic expectations before scaling.