The speed of development is overflowing security and raising critical vulnerabilities

Recent data from OX Security, which examined 216 million safety findings in 250 organizations over a quarter, draw a disturbing reality for development and security teams: the gross volume of alerts has grown significantly, but the most worrying is that the number of critical risks prioritized has increased much faster. While total alerts increased by about 52% per year, the portion of findings that actually represent a high risk to the business climbed almost four times, a gap that requires rethinking how the software insecurity is prioritized and repaired.

The era of speed brings a new kind of trouble. The accelerated adoption of IA development assistance tools is accelerating the creation of code and thus the emergence of more complex and context-dependent vulnerabilities. OX Security notes a clear relationship between the use of programming automatic assistants and the increase in critical findings: the average per organization increased from just over 200 to almost 800. This does not mean that IA is inherently insecure, but increases the speed and density of change, and many traditional analytical practices are not prepared for that rate.

This gap between production speed and remediation capacity is what some already call a "speed gap." When the pace of change exceeds the ability of safety workflows to detect, prioritize and fix, the proportion of really dangerous problems grows faster than the tools that detect them. As a result, the proportion of critical findings on total alerts almost tripled in the analysis of OX, moving from a tiny fraction to a figure requiring operational attention.

It matters less the technical score and more the business context. Traditionally, organizations have relied on metrics such as CVSS to order risks, but recent findings confirm something that many practitioners already suspected: technical gravity alone does not define the risk for the company. Factors such as whether a component processes sensitive personal data or is part of a high priority business application are raising findings to critical category much more often. That a vulnerability exists in a service that PII manages or in a piece of the banking core radically changes the urgency of its mitigation. This context prioritization approach is in line with the recommendations of communities such as OWASP on real-risk business approaches ( OWASP Risk Rating Methodology).

The attention to personal data is not casual: the processing of PII was one of the factors that increased critical findings. From a regulatory and reputational point of view, exposing personal information generates direct impact and sanctions, as is recalled by guides from agencies such as NIST on the processing of identifiable personal information ( NIST SP 800-122), so prioritizing according to the sensitivity of the data is now an indispensable practice.

Sectoral disparities and the case of the car. OX analysis also shows that the risk is not evenly distributed across sectors. The insurance firms presented the highest density of critical findings, probably because of the convergence between legacy systems that treat sensitive data and new digital platforms. For its part, the automotive sector generated the highest gross volume of alerts, which makes sense when considering the explosion of software within modern vehicles: cars are becoming software-defined platforms and that multiplies the attack surface. Several industrial analyses have highlighted this phenomenon of rapid expansion of software in the car and the implications for safety and quality ( McKinsey: software-defined vehicles).

All this leads to a practical conclusion: the simple accumulation of scans and rules is no longer enough. Linking tools and inherited scanners remain useful for filtering obvious problems, but the priority must be to understand the purpose of the service, its exposure to sensitive data and its critical business. This contextual look requires integrating signals beyond technical vulnerability, including deployment telemetry, unit mapping and asset classification according to their impact.

What should technical equipment change? First, priority policies must evolve: stop treating all vulnerabilities with the same metric and adopt models that weigh the location of the failure and the sensitivity of the environment. Secondly, security must enter the development cycle earlier and accompany the acceleration provided by the IA. Integrating security controls into CI / CD, adding contextual analysis and automating part of the triage can dampen the speed gap. Thirdly, it is not enough to detect: it is crucial to close the loop with the remedy and verification in production, and where necessary, to deploy business risk-oriented compensatory mitigation.

Finally, the community and industry must continue to investigate the impact of IA tools on software security. Platforms offering programming assistants have started publishing guides and safety considerations for responsible use, and teams should combine these recommendations with rigorous internal controls ( GitHub Copilot's security and privacy guide). Furthermore, industry reports on the state of application security, such as those of companies specialized in analysis and testing, can serve as a reference for understanding trends and adapting practices.

The analysis of OX Security, the complete version of which with methodology and sectoral measurements is available on its website ( OX Security), is a strong reminder: the transformation of development driven by IA and the increasing complexity of the software require a change in the way we value, prioritize and fix vulnerabilities. The goal is no longer just to reduce the number of alerts, but to identify and mitigate what can really harm the business and the people it serves.