Modern DevSecOps needs security checks that run before release day. Teams now write code, build services and deploy updates at a pace that manual review cannot match. That’s why they use automated testing, as it helps catch routine flaws before they reach production.
The pressure has grown. Verizon’s 2025 Data Breach Investigations Report found that vulnerability exploitation caused 20 percent of breaches as an initial access route, up 34 percent from the prior report. It also found that credential abuse caused 22 percent, which shows why code flaws and access flaws need attention together.
Automated testing has become more valuable as software teams release changes faster. Services like XBOW support that work by mapping application surfaces, testing likely attack routes and validating whether a finding can lead to real access. For security professionals, the benefit lies in better proof, fewer vague tickets and faster handoffs to engineering teams.
Start with code testing
Static application security testing checks source code before the software runs. It can find weak input handling, unsafe functions and risky patterns in pull requests. Developers value this because the test happens near the line that caused the issue. Nobody enjoys reopening a ticket three weeks after the code has travelled through six approvals.
Static testing works best when teams tune rules. A scanner that flags every minor issue will lose trust. A good setup focuses on high-risk patterns, clear fixes and ownership. OWASP’s DevSecOps guidance places security testing inside the pipeline so teams can find issues during development instead of waiting for a later review.
Test the running application
Dynamic application security testing checks a live application from the outside. It sends requests to a running service and looks for unsafe responses. This helps teams find flaws that code review may miss, such as broken access checks or unsafe redirects.
Dynamic testing needs care because it touches real systems. Teams should test staging environments where possible, set safe limits and record what the tool did. The value comes from proof. A finding that shows the tested request, the response and the affected route gives developers a concrete starting point.
Platforms like Xbow fit this part of the toolset when teams need automated penetration testing for web applications. The platform describes controlled, non-destructive validation before surfacing findings, which supports a stronger link between test output and real exploitability.
Check dependencies before they check you
Software composition analysis reviews third-party libraries and open-source packages. That matters because most modern applications depend on code that no internal team wrote. A package can save time, but it can also bring a known flaw into a build.
CISA’s Known Exploited Vulnerabilities catalog gives teams a practical source for prioritising flaws that attackers have used in the wild. Security teams should use that kind of evidence when they decide which dependency updates need urgent work.
Dependency testing should run in pull requests and scheduled checks. A project may pass today, then become exposed next month after a new advisory. Automated checks help teams catch that change without asking someone to reread every package list by hand.
Protect secrets and build settings
Secret scanning checks code and configuration for passwords, tokens and keys. This has become a basic need because one exposed token can give an attacker access without a software bug. A 2025 report from TechRadar described research that found more than 17,000 exposed secrets across public repositories and indexed web data.
Infrastructure-as-code testing checks cloud templates and deployment files. In plain terms, it looks at the instructions that build servers and services. This can catch open storage, weak identity rules and risky network settings before deployment. The best tests show both the risky line and the safer option.
Use AI with limits
Advancements in AI have led automated testing has started to move from pattern matching toward reasoning. AI can help tools explore more paths, draft clearer remediation notes and test combinations that older scanners may miss. It can also create confidence that the evidence has earned.
That promise needs discipline. The Guardian reported in May 2026 that Google had warned about AI-powered hacking reaching industrial strength, with criminal and state-linked actors using advanced models to improve malware and exploit work. Defensive teams therefore need automation that can keep pace, but they still need humans to approve scope and judge impact.
Modern platforms, including Xbow, use AI to simulate attacker behaviour across web targets and then validate findings before reporting them. That supports DevSecOps teams that need faster tests without turning every alert into a meeting. The right outcome is fewer unclear findings rather than more alerts.
Prioritise attack paths
Many teams still rank issues by severity score alone. That can mislead. A medium issue that links to exposed credentials may matter more than a severe issue blocked by access controls. Attack path analysis looks at how flaws connect.
This approach helps business leaders understand risk. They need to know whether an attacker can reach customer data, change production code or take over an account. A good automated tool should make that path visible and show the control that breaks it.
IBM’s 2025 Cost of a Data Breach Report put the global average breach cost at $4.44 million. That number gives leaders a reason to fund testing, but the daily work still comes down to fixing reachable risks before attackers use them.