As artificial intelligence systems improve at spotting security flaws, a growing chorus of experts says the software industry may need to change how it builds code from the ground up. The warning comes as machine learning tools begin flagging bugs faster, at greater scale, and with fewer misses, raising new questions about safety, ethics, and the future of secure design.
The core concern is simple: if machines can find holes in minutes that once took skilled researchers days, both defenders and attackers gain new power. Companies face pressure to design software that is safer at its core, rather than trying to patch problems after release.
“AI models are getting so good at finding vulnerabilities that some experts say the tech industry might need to rethink how software is built.”
Why Security Is Shifting
Software security has often depended on human code reviews, testing, and post-release patching. Over the past decade, automated scanners, fuzzing tools, and secure coding practices have become more common. Now, large language models and other AI systems can read code, generate test cases, and suggest fixes at scale.
This shift changes the speed and cost of finding flaws. It also raises the risk that criminals will use the same tools to scan open-source projects, cloud apps, and mobile software for weak points.
Security leaders point to long-standing issues—memory safety errors, weak authentication, and poor input validation—that AI can surface quickly. They also warn that deeper design problems, such as insecure defaults and complex supply chains, still require human judgment and better architecture.
How AI Is Finding More Bugs
New tools combine pattern recognition with code analysis. They can read large codebases and suggest where mistakes are likely, from injection risks to broken access controls. Some can generate exploit proofs-of-concept, helping teams confirm a bug is real and needs a fix.
These systems can also analyze dependencies. That matters as modern apps rely on many libraries and services, where one weak link can affect thousands of products.
Experts say the biggest gains show up when AI augments human teams. Engineers validate results, set priorities, and decide how to fix issues without breaking features.
Calls for Secure-by-Design Practices
The push to “shift left” on security—moving checks earlier in development—may accelerate. Advocates argue that safer defaults, strict access controls, and memory-safe languages can help cut entire classes of bugs before they ship.
Key changes many teams are weighing include:
- Using memory-safe languages for new code and high-risk modules.
- Enforcing secure defaults for authentication, encryption, and logging.
- Automating tests and code review with AI in continuous integration.
- Tracking software bills of materials to manage supply chain risk.
Critics warn that AI can generate false positives and overwhelm engineers. Others fear over-reliance on tools may dull skills. There is also concern about dual-use: the same methods that help defenders can aid attackers.
Industry Response Gathers Pace
Some companies are training developers to use AI-assisted code review and to verify findings with manual checks. Security teams are updating playbooks to handle faster discovery cycles and to patch issues more quickly, sometimes with automated fixes.
Regulators and standards bodies have signaled support for secure-by-design ideas, urging vendors to ship safer products rather than relying on users to manage risk. That could influence procurement rules and liability debates over time.
Open-source communities are also experimenting with AI triage for bug reports and dependency alerts. The challenge is to balance speed with accuracy and to avoid flooding maintainers with noise.
What Comes Next
Observers expect AI to push security toward continuous verification. That could include real-time scanning in production, automatic isolation of suspicious behavior, and “self-healing” patches for known patterns.
Longer term, architecture choices may matter most. Simpler designs, strong sandboxing, and clear privilege boundaries can blunt the impact of bugs that slip through. The goal is to reduce the blast radius, not just find more flaws.
Multiple Viewpoints, One Pressure Point
Optimists see a chance to cut common errors and raise the floor for software safety. Skeptics worry that attackers will scale faster and that automation will hide fragile systems under a layer of quick fixes. Both sides agree on one point: the cost of ignoring design will rise as AI accelerates the pace of discovery.
For now, the message is clear. As machines raise the bar for finding vulnerabilities, teams must respond by building safer software from the start. Expect more firms to pilot AI-assisted reviews, adopt safer defaults, and revisit language and dependency choices. The next test will be whether these steps lead to fewer critical bugs in the wild and faster, safer updates when issues arise.
