When AI coding races ahead and security struggles to keep up

AI-assisted coding accelerates software development, widening the engineering–security gap

The speed of software development has taken a massive leap forward. AI-driven coding tools are now operating as accelerators, helping engineering teams deliver software faster than ever before. ProjectDiscovery’s 2026 AI Coding Impact Report shows that every company surveyed reported faster software delivery in the last year. Nearly half of them, 49%—credited AI-assisted coding tools for most or all of that growth. That’s real, measurable acceleration.

But there’s an issue that business leaders cannot ignore. Security teams are not scaling at the same rate. Around 62% of security professionals said it’s getting harder to review all the new code being pushed out. If engineering teams are running at full speed and security is holding on to the rear bumper, risk starts to rise quietly but fast. This imbalance creates what’s best described as an internal drag: production races ahead, while oversight struggles to keep up.

For executives, this presents a strategic challenge. Faster product cycles are great for market advantage, but they come with hidden risk if security frameworks can’t keep pace. Companies need to realign priorities, accelerating not only their development capabilities but also their risk management and security validation. The takeaway is simple: innovation and protection must scale together. Being first to market is good, but staying secure while growing fast is what ultimately sustains the advantage.

Security teams are overburdened with manual work, undermining vulnerability remediation

Security teams today are drowning in manual work. The automation that has powered engineering forward hasn’t reached them yet. The numbers from ProjectDiscovery’s survey are clear: two-thirds of cybersecurity practitioners, 66%—spend more than half their work hours validating security findings instead of fixing the real problems. That’s a lot of human capital tied up in verification rather than action.

Each week, most teams are focused on reactive tasks. Sixty percent triage alerts. Fifty-three percent coordinate fixes. Forty-six percent validate exploitability. These numbers show what many in the industry already feel daily, security teams are overloaded with alerts, false positives, and coordination work. The real work, fixing vulnerabilities and securing systems, gets delayed. The result is predictable: delayed remediation and growing backlog.

For executives, this is not just a workforce efficiency issue, it’s a business continuity risk. When security experts are trapped in manual loops, the organization becomes more vulnerable. Leaders must make technology investments that reduce noise, streamline validation, and create a balance between discovery and remediation. That’s where smart automation comes in, not automation that floods teams with more alerts, but systems that surface evidence-based insights and help teams act on what truly matters.

Long-term competitiveness depends on how effectively an organization can protect its digital assets without slowing progress. The companies that figure out how to automate security validation intelligently will move faster, stay safer, and outpace those still caught in manual cycles.

Trust and transparency issues hinder the adoption of AI tools in security workflows

Trust is becoming the main barrier holding back AI integration in cybersecurity. While most security professionals see clear potential in AI to handle growing workloads, they remain cautious. They want to understand how these systems make decisions, what actions are taken, and how those actions are recorded. In ProjectDiscovery’s 2026 AI Coding Impact Report, 57% of respondents said they would only trust AI-based penetration testing tools if those systems provided full audit trails of their activity. That signal from the field is unambiguous: without transparency, adoption will stall.

Security leaders recognize that automated tools can reduce human burden, but they also know that a lack of visibility can introduce new risks, especially when the decisions of AI-driven systems affect how threats are prioritized or reported. Practitioners cannot rely on tools that operate in opaque ways, since one poor or unexplained automated choice could alter the organization’s overall risk posture.

For C-suite executives, this is a governance issue as much as a technology question. AI adoption in security needs the same level of oversight that financial or compliance systems receive. Leaders should demand explainable AI, tools built with auditable data trails that can withstand external scrutiny. This isn’t only about satisfying compliance officers; it’s about building sustainable automation that security teams can trust.

The report further highlights how AI-assisted coding increases potential risk areas. Exposure of secrets was ranked as the number one challenge, cited by 78% of respondents. Executives must ensure that as AI accelerates development velocity, there is an equally strong framework around data protection, visibility, and accountability. Transparency must evolve from being an afterthought to becoming a core requirement of any AI integration across the organization.

The imbalance between rapid development and slower security scaling is straining organizational capacity

As AI accelerates software production, security operations are struggling to expand in parallel. The imbalance is especially visible in mid-sized organizations, which often lack the depth of resources large enterprises possess. Around 69% of respondents from mid-sized companies said it is increasingly difficult to keep up with the growing volume of code that requires security review. This widening gap is creating more risk pressure across multiple layers of the business.

For executives, the implication is straightforward: the current pace of AI-driven development demands a new approach to scaling security functions. Hiring more people is not a sustainable solution. The answer lies in rethinking processes, upgrading automation, and integrating tools capable of matching development velocity without sacrificing accuracy or control.

Security teams are now facing heavier code volumes, more alerts, and higher rates of false positives. Each of these issues drains time and attention from genuine threats. Without process redesign, teams will remain in constant reactive mode, and vulnerabilities will grow faster than they can be resolved. Leadership must focus on enabling security functions to scale flexibly with the same efficiency that engineering teams have achieved through AI.

The strategic goal should be alignment, syncing development speed with security capacity. Organizations that achieve that will reduce operational disruption, maintain resilience, and continue innovating without accumulating hidden risk. Failing to align these functions will lead to more stress, longer remediation cycles, and a higher probability of security incidents at scale.

Post-detection processes, rather than vulnerability identification, are the critical bottleneck

The heart of today’s cybersecurity challenge isn’t finding vulnerabilities, it’s dealing with them efficiently after discovery. Detection capabilities have improved dramatically, powered by automation and AI tools that uncover weaknesses at a scale never seen before. The difficulty now lies in what happens next: validating which vulnerabilities truly matter and remediating them quickly. ProjectDiscovery’s data reveals that the biggest choke point in security workflows appears after vulnerabilities are found, not during initial detection.

Rishi Sharma, Chief Executive Officer and Co-Founder of ProjectDiscovery, captured this insight directly, stating that “the industry spends a lot of oxygen talking about finding more vulnerabilities, but our data shows the real bottleneck is downstream.” His view aligns with what many security leaders already experience, teams are overwhelmed by the volume of findings, much of which require manual validation before any fix can proceed. That delay compounds over time and leaves organizations exposed, even as their scanning tools become more advanced.

For business leaders, the solution involves focusing investment where it will make the most operational impact: in validation and remediation systems. Rather than expanding detection layers or adding new scanners, organizations should be improving automation that distinguishes genuine issues from noise and elevates verified, actionable intelligence to the top of the queue. This approach reduces fatigue among security staff and shortens the time between detection and resolution.

Executives should recognize that faster production cycles powered by AI must be matched by smarter, evidence-driven remediation tools. Automation that produces verified results, not just more alerts, will define the next generation of effective cybersecurity. Security can no longer operate as a secondary consideration after code is delivered; it must evolve architecturally to move at the same speed. Those companies that bring detection, validation, and remediation into alignment will create a resilient, self-correcting system capable of keeping pace with innovation without increasing exposure.

Key executive takeaways

• AI-driven speed is widening the security gap: AI-assisted coding has accelerated software delivery across industries, but security operations are unable to match that pace. Leaders should ensure that cybersecurity frameworks scale in step with engineering speed to prevent compounding risk exposure.
• Manual security work is limiting progress: Two-thirds of security practitioners spend most of their time validating findings instead of fixing issues. Executives should invest in smarter automation that reduces repetitive work and shifts focus toward faster vulnerability remediation.
• Lack of trust is slowing AI adoption in security: Most security professionals hesitate to rely on AI tools without full auditability or visible decision processes. Decision-makers should prioritize AI systems that provide transparency and traceability to build trust and readiness for wider adoption.
• Scaling imbalance is stressing security operations: As code volumes increase, security teams, especially in mid-sized firms, struggle to keep up, raising risk exposure. Leaders need to strengthen security processes, align team capacity with engineering output, and embrace tools that can scale efficiently.
• Post-detection processes remain the real bottleneck: The issue isn’t in finding vulnerabilities but in validating and fixing them. Executives should focus investment in automation that filters false positives and accelerates remediation, aligning security execution with the speed of AI-driven innovation.