Artificial intelligence came at just about the right time, speeding up app and software development as the world started to contend with skills shortages, but it changed the pace so much that security teams have not been able to keep up.
Recently, we’ve seen AI being applied across multiple other domains with role-specific agents and tools, but that’s introduced its own challenges. While tools like Claude Code have proven a hit for generating, reviewing and editing code in seconds, security-focused tools like Anthropic’s Claude Mythos family of models are having broader impacts on the industry.
Anthropic itself has even admitted that Mythos is so powerful that the worry it could be abused by malicious criminals is extremely real – the Preview model is currently only available to a select number of pre-approved partners.
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So with AI now capable of inspecting code, discovering vulnerabilities and suggesting fixes, do organizations even need as many human workers on the case, or can they get by with significantly fewer humans in the loop serving as AI reviewers? Recent layoffs have certainly implied as much.
The evolving role of security workers in an AI-first world
But with the entire lifecycle of development now amplified by AI, experts are warning that companies could actually be creating more work for themselves, and more than they could ever handle, leaving them facing strains from angles they weren’t previously exposed to.
For example, fewer than one in 10 companies now fix 90% of identified vulnerabilities within 90 days – implying that the volume of vulnerabilities is indeed increasing, rather than that fix efficiency is slipping.
Anthropic even revealed that its around 50 early Mythos Preview partners discovered more than 10,000 high- or critical-severity vulnerabilities – and thousands more of lesser significance.
Checkmarx CEO Sandeep Johri predicts we could soon find a balance, where vulnerabilities volume matters less and we revert our focus back toward exploitable risks. I spoke with Johri about the evolution of AppSec, where AI is and isn’t useful, and how organizations can balance speed and control.
- With the rise of AI coding tools and AI-generated software, some are questioning whether traditional application security practices are becoming outdated. Is AppSec actually becoming obsolete, or is it evolving?
Traditional application security is not obsolete. It is evolving to meet the reality of how software is being built today.
For years, the process was fairly linear: developers wrote code, security teams scanned it, and vulnerabilities were addressed later. That approach becomes much harder when software is being created at a much faster pace with the help of AI.
AI accelerates development and risk simultaneously: 70% of developers say AI-generated code created more vulnerabilities in 2025, according to our research. As code volume and complexity compound, security needs to move earlier into the development process, giving developers the tools and guidance they need while they are building.
Security teams will continue to play a critical role to help organizations develop software and maintain confidence in their enterprise applications. But their focus needs to shift from finding vulnerabilities to remediating them at scale, because we are tracking an enormous gap in most companies. Our data finds that fewer than 10% of organizations fix 90% of identified vulnerabilities in 90 days.
- AI coding tools are helping developers create software faster than ever before. What new security challenges does this introduce for organizations adopting these technologies at scale?
The biggest challenge is that development speed is increasing faster than many security processes can keep up with. AI coding tools allow teams to create and deploy software quickly, but the code generated by AI still needs to be reviewed, tested, and secured.
Companies that ship 81-100% of their code with AI are nearly three times more likely to ship vulnerable code than those who use AI 1-20% of the time. This volume can overwhelm security teams with thousands of findings, many of which don’t represent meaningful risk. The priority needs to be identifying the vulnerabilities that actually create exposure and helping teams fix those issues faster.
- Many organizations are looking to AI to help identify and fix security vulnerabilities. Why shouldn’t companies rely solely on AI models to secure the code that AI is helping create?
AI is a valuable tool for security teams, but organizations still need accuracy, context, and human oversight. AI can help identify patterns, analyze code, and accelerate remediation, but security decisions require confidence in what risks actually matter.
Frontier models can uncover hidden exploit paths, but they can also deliver inconsistent findings and false positives. Their results may change depending on the prompt, and they can still miss known critical vulnerabilities.
The challenge with relying only on AI is that organizations may create a false sense of security, or “automation bias.” AI models can generate code and help analyze vulnerabilities, but they need to be paired with security expertise and proven security practices.
The most effective approach combines AI-driven capabilities with strong security foundations, so teams can move faster while maintaining control over risk.
- As companies adopt more AI tools throughout the development process, what are the biggest security risks they need to consider beyond just AI-generated code?
Organizations need to think beyond the code itself and look at the entire AI ecosystem being introduced into software development. Many companies are adopting AI tools, models, agents, libraries, and other components faster than they can establish governance around them. This creates visibility challenges because security teams may not know what AI technologies are being used, where they exist in applications, or whether they meet security requirements.
Another concern is shadow AI, where employees use AI tools without formal approval or oversight. Organizations need visibility, clear policies, and a way to manage these technologies as part of their overall software supply chain.
Perhaps the most urgent problem is the expansion of the attack surface itself. With LLMs, it has never been faster, cheaper, or easier for bad actors to exploit software. Issues that sat undetected for years are now being surfaced and weaponized at machine speed. Of the vulnerabilities Mythos has found so far, 99% haven’t been patched, according to Gartner.
- How does the rise of AI change the role of security teams? Does the traditional approach to finding vulnerabilities need to shift toward a model focused more on prioritization, remediation, and continuous protection?
Identifying vulnerabilities is no longer enough when organizations already have more findings than they can realistically address. Security has to become continuous, embedded in development workflows, working in lockstep with developers, to build securely from the start while maintaining visibility and control.
We are shifting the focus to understand which issues create the greatest risk to give developers the context to address them fast, where code is written in the IDE.
Fidelity now matters more than volume. One verified true positive is worth more than a hundred low-confidence findings. If developers can’t trust what they’re shown, they’ll start ignoring it. That’s why organizations are increasingly looking at metrics like F1 score, which measure precision and recall together, rather than raw finding counts.
- What does the future of application security look like in an AI-driven software development world? Will organizations need a different approach to balancing speed, innovation, and security?
The future of application security will require a more integrated approach. Organizations are going to continue adopting AI because the productivity benefits are significant, but security needs to evolve alongside that innovation.
Security will become more agentic, more intelligent, and more closely connected to the development process. Part of that evolution is combining deterministic, rules-based scanning with AI-driven reasoning in a single process, rather than running them as separate, disconnected tools. Deterministic methods catch what’s already proven; AI reasoning catches what’s novel. Together they’re more complete than either alone.
In addition, deterministic models have real cost advantages. Asking a frontier model to reason its way to security (i.e. extra review passes, self-generated threat models) burns tokens fast. That cost compounds the longer a vulnerability survives: cheap to fix in the IDE, more expensive in CI/CD, most expensive once it’s live in runtime. And every time a developer has to stop and pull a vulnerability out of code that’s already shipped, that’s velocity lost to rework instead of innovation.
Teams will need technology that can help identify real risks, support faster remediation, and provide visibility across the entire software lifecycle. The organizations that succeed will be those that make security part of how they build software, allowing developers to move quickly while reducing unnecessary risk.
- For organizations that are embracing AI coding tools today, what steps should they take to make sure they can innovate quickly without introducing unnecessary security risks?
The first step is visibility. Organizations need to understand where AI is being used, what tools are being introduced, and what impact those tools have on their applications.
Remediation is far cheaper the earlier it happens — catching an issue in the IDE costs a fraction of catching it further down the pipeline. But there’s another unsettling gap in our research: nearly all developers have access to in-IDE security tools, but fewer than one in five actually secure code as they write it. The cost of fixing that issue compounds as it passes through later stages of development.
In addition, organizations need clear governance around AI adoption, because only 22% currently have formal AI governance policies in place. That means defining policies, monitoring usage, and making sure teams have the right security controls as they continue to innovate.
AI will continue to change software development. The companies that benefit most will be the ones that embrace the technology while building security into the process from the beginning.
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