Using AI to Improve Software Security: Why Regular Code Analysis Matters More Than Ever

Written by: Danny Kleine
Best practices Infrastructure

Every piece of software your business relies on contains code. And somewhere in that code, there are almost certainly security holes that no one has found yet. For years, finding those weak spots required expensive specialists, slow audits, or automated tools that produced more noise than useful results. That is changing fast.

Recent AI models can now scan, analyze, and help fix security issues in software at a speed and depth that simply was not possible before. For small and medium-sized businesses, this matters a lot. You no longer need a large dedicated security team to keep your applications safe, but you do need to make security checks a regular part of how your software is built and maintained.

What a single vulnerability can cost you

Before looking at what has changed, it is worth understanding what is at stake. According to IBM's 2025 Cost of a Data Breach Report, a data breach costs the average company around €4 million. For smaller companies with fewer than 500 employees, the figure is roughly €3 million, and those companies spent 13% more on dealing with breaches compared to the year before.

That €3 million includes the obvious things like forensic investigation and system recovery, but also the costs that are harder to measure: lost business during downtime, customers who leave, legal fees, and regulatory fines. Under the NIS2 directive, which is now enforceable across the EU, companies can face penalties of up to €10 million or 2% of global turnover. Under GDPR, fines can go even higher.

On top of the money, there is the time. The average breach takes about 241 days to detect and contain. That is eight months where an attacker may have access to your systems before anyone notices. Breaches involving stolen login credentials take even longer, nearly 300 days on average.

These numbers hit smaller companies harder. According to ENISA's Threat Landscape 2025 report, SMEs are increasingly attractive targets for cybercriminals, especially ransomware groups who adjust their demands to match what a smaller business can pay. Over 70% of ransomware incidents now involve SMEs. In Germany alone, cybercrime and sabotage cost businesses an estimated €267 billion over the past year, a 29% increase from the year before. And the way most attackers get in is not particularly clever: phishing accounts for 60% of initial intrusions across Europe, and exploiting known but unpatched vulnerabilities makes up another 21%.

How software security worked before AI

To understand the impact of AI on software security, it helps to know the tools and methods that came before it. Traditional approaches to finding vulnerabilities generally fall into a few categories.

Static Application Security Testing (SAST) tools analyze source code without running the application. They look for known patterns like hardcoded passwords, SQL injection risks, or insecure data handling by matching code against a set of predefined rules. Tools like SonarQube, Checkmarx, and Veracode have been widely used for years. They are good at catching common mistakes early on, but they have a clear limitation: they can only find what they already know to look for. More complex issues, like logic flaws or broken access control, often slip through.

Dynamic Application Security Testing (DAST) works differently. Instead of reading the code, it interacts with the running application from the outside, testing it the way an attacker would. Tools like Burp Suite and OWASP ZAP are popular for this purpose. DAST is better at finding issues that only show up at runtime, but it can be slow, produces a lot of false alarms, and still misses problems that require understanding how the application actually works under the hood.

Manual penetration testing has always been considered the best option. A skilled security researcher examines your application, thinks creatively about how it could be broken, and reports what they find. This approach catches things that automated tools miss. The downside is that it is expensive and hard to scale. A thorough manual review of even a mid-sized application takes days or weeks, and experienced security researchers are hard to find. Industry estimates put the global shortage of cybersecurity professionals at roughly 3.5 million.

These methods have served the industry well for over twenty years, and they remain useful. At solutions.io, we still use static analysis and manual code review as part of our regular support and monitoring work. But on their own, these tools share a common weakness: they lack context. They can tell you that a line of code matches a known bad pattern, or that a URL responded in an unexpected way, but they cannot truly read and understand your code the way a human expert would.

What AI changes about code analysis

This is where recent AI models have made a real difference. Modern AI can read code, understand how different parts of an application work together, trace how data moves through the system, and spot security problems that only become visible when you look at the bigger picture. These models do not just match patterns. They reason about what the code is supposed to do and where it falls short.

In practice, this means a development team can now point an AI tool at an existing codebase and get results that go well beyond what older scanners would find. The AI can identify broken access controls, spot gaps in authentication logic, flag unsafe data handling that only becomes obvious when you understand how several modules interact, and suggest concrete fixes. All of this happens in a fraction of the time a manual review would take.

For teams already using AI coding tools, this fits naturally into existing workflows. You connect the tool to your code repository, it analyzes everything in context, and it returns a list of findings with clear explanations and suggested patches. This works especially well in setups where automated testing already runs on every code change. In our own projects, we have started combining AI-assisted code review with our existing CI/CD pipelines, and the results have been noticeably better than what we got from static analysis alone, especially for catching issues in how different parts of an application interact.

Anthropic recently launched Claude Code Security, which is a good example of this approach. Instead of scanning for known patterns the way traditional tools do, it reads and reasons about code in context, much like a human researcher would. Using their Claude Opus model, Anthropic's team found over 500 vulnerabilities in widely-used open-source software, many of which had been missed for years despite regular expert review.

OpenAI has built something similar. Their tool, originally called Aardvark and now renamed to Codex Security, works in stages: it first builds a threat model of the project, then scans code changes against the full context of the repository, and finally tries to reproduce any issues it finds in a sandboxed test environment. During testing, it confirmed real vulnerabilities in open-source projects, with ten of them receiving official CVE identifiers.

These are not experimental projects. These tools are already being used in production, and they are finding real problems that older approaches missed.

Faster fixes with AI agents

Finding vulnerabilities is only half the job. Fixing them is what actually makes your software safer. This is another area where AI tools are making a big difference.

In a traditional workflow, a security scan produces a report. A developer then needs to understand the problem, find the relevant code, work out the right fix, apply it, and make sure it does not break anything else. When you have dozens or hundreds of findings in a backlog, this can take weeks.

AI tools can speed this up a lot. Once a vulnerability is found, the same AI that spotted it can often suggest a specific fix that addresses the issue without causing new problems. The developer reviews the suggestion, checks it, and merges it. What used to take days per issue can now often be done in minutes.

This does not mean taking developers out of the process. A human still reviews and approves every change. But the time-consuming part, understanding the problem, tracing its effects through the codebase, and writing the fix, is handled by the AI. For small teams without a dedicated security person, this makes a huge difference. We see this with our own clients: many of them run applications built over several years with a small development team. Sitting down to work through a backlog of security findings used to mean pulling developers away from feature work for weeks. With AI-assisted analysis and suggested fixes, we can now work through those lists much faster while still keeping a human in control of every change.

The future: better models, bigger risks

AI capabilities in security are moving fast, and they cut both ways. Better models mean better defenses, but they also give attackers better tools.

Anthropic's Project Glasswing, announced in April 2026, makes this very clear. The project was started because of what they observed in an unreleased model called Claude Mythos Preview, which showed it could find and exploit software vulnerabilities better than most human security researchers. The model found thousands of previously unknown vulnerabilities in every major operating system and web browser. One of them was a flaw in OpenBSD that had been there for 27 years. Another was in FFmpeg, a video processing library used in countless applications, where the bug had survived 16 years and millions of automated tests.

Project Glasswing brings together some of the biggest names in tech, including AWS, Apple, Google, Microsoft, CrowdStrike, Cisco, and Palo Alto Networks, in a joint effort to use these AI capabilities for defense rather than attack. Anthropic has committed up to $100 million in credits and donations to support this work, including direct funding for open-source security organizations.

The takeaway for business owners is not about the details of any single model. It is about where things are heading. AI that can find and exploit security holes at this level already exists. Over time, similar capabilities will become more widely available. The question is not whether your software will be scanned by AI tools. It is whether your team will scan it first, or whether someone with bad intentions will.

Why regular updates and analysis matter now more than ever

All of this leads to one clear conclusion: software security is not something you do once and forget about. It is an ongoing process that needs to be part of how your applications are built, released, and maintained.

For existing software, this means running security scans regularly, not just once a year for a compliance checkbox, but as a normal part of your development process. Every dependency update, every new feature, every change to your server setup can introduce new vulnerabilities. AI-based scanning makes it realistic to check for issues on every code change, catching problems before they go live.

For new software, the opportunity is even bigger. When you include AI security analysis from the start of a project, you build software that is more secure from day one. Problems get caught and fixed while they are small and easy to deal with, instead of being discovered months later when they are deeply woven into your application.

For third-party libraries, regular checking is essential. Most modern web applications depend heavily on open-source packages. A vulnerability in a popular library can affect thousands of applications at once, and we have seen this happen repeatedly with packages in ecosystems like npm, PyPI, and Composer. Keeping your dependencies updated and scanning for known issues in the packages you use is not something you can skip. IBM's 2026 report noted a nearly fourfold increase in supply chain and third-party compromises since 2020.

The practical steps are simple. Keep your software and its dependencies updated. Add automated security scanning to your build and deployment process. Use AI tools to go beyond what traditional scanners can find. Schedule periodic deeper reviews, whether done by your own team, an external partner, or an AI tool, to catch the subtler issues that quick scans miss. And treat security findings as real work items, not as something to get to "when there is time."

If you do not have the capacity to do this yourself, this is exactly the kind of work we do at solutions.io. Our support and monitoring service includes ongoing security monitoring, dependency management, and regular application reviews. We keep an eye on your applications so your team can focus on building.

What this means for your business

If you run a business that depends on software, and in 2026 almost every business does, the security situation has changed. Attacks are more automated and more frequent. But the tools to defend against them have gotten better too.

AI has not made software security effortless, but it has made it much more practical. A small development team can now keep their applications secure at a level that would have required a whole security department just a few years ago. The combination of AI scanning, suggested fixes, and continuous monitoring puts serious security within reach for businesses of any size.

The most important step is the simplest one: start. If your applications are not being regularly scanned and updated, they are building up risk every month. Whether you handle this internally or work with a partner, making security analysis a regular part of your software process is no longer something nice to have. It is a necessity.

At solutions.io, we help businesses keep their software secure, stable, and up to date. From security audits and monitoring to ongoing support and maintenance, we work as part of your team to keep your applications protected. Get in touch to discuss how we can help.

Frequently asked questions

How often should we scan our software for security issues? Ideally, on every code change as part of your automated build process. At a minimum, run a thorough scan monthly and after every major update or dependency change. Annual audits alone are not enough anymore, as new vulnerabilities are discovered daily.

Can AI tools replace manual security testing? Not entirely. AI tools are very good at scanning large codebases quickly and catching issues that traditional scanners miss. But they work best as a complement to human review, not a replacement. A good approach is to use AI for continuous scanning and bring in a human expert for periodic deeper analysis.

Is this only relevant for companies that build their own software? No. If you run a website, a web application, or any system that uses third-party software or open-source libraries, you are exposed to the same risks. Vulnerabilities in a popular framework or plugin can affect your application even if you did not write the vulnerable code yourself.

What is the NIS2 directive and does it apply to my company? NIS2 is an EU directive that sets cybersecurity requirements for companies in essential and important sectors, including digital infrastructure, IT services, and manufacturing. It applies to a much broader range of companies than its predecessor and introduces personal liability for management. If you operate in the EU and have more than 50 employees or over €10 million in revenue, it is worth checking whether you fall under its scope.

What does a security audit from solutions.io involve? We review your application code, server configuration, dependency chain, and deployment process. We combine traditional testing methods with AI-assisted analysis to identify vulnerabilities and provide a clear report with prioritized findings and recommended fixes. For ongoing protection, our support and monitoring service includes continuous monitoring and regular updates.

Sources and further reading