As artificial intelligence coding tools are rapidly adopted, development teams are able to develop software more quickly, however, cybersecurity teams are also becoming increasingly under pressure due to the increasing volume of AI-generated code. Open-source dependencies have become increasingly common, and their rapid introduction can result in increased vulnerabilities for an organization and additional remediation workloads. This is a key concern.  A study involving 300 security and engineering leaders in technology, financial services, healthcare, manufacturing, and government sectors revealed the growing challenges associated with managing this risk. Organizations may face remediation debt if dependencies accumulate faster than security teams can assess and resolve them, which could adversely affect security operations, compliance, productivity, and overall business resilience.  AI coding systems are expected to become increasingly autonomous as time goes on, resulting in software and dependencies being introduced into enterprise environments at a pace that existing security processes may not be able to keep up with. Prioritization is also becoming increasingly important in light of the growing number of vulnerabilities. If every CVE is treated equally, security resources will be consumed rapidly without addressing the weaknesses that are most significant.  There are several factors that can be considered in determining which issues require immediate action, including whether the vulnerability is reachable, whether the flaw can be exploited, and which systems or data will be impacted. Security controls are also becoming more involved in software development earlier. Testing static applications for security and analysis of software composition can be incorporated into development environments and continuous integration/continuous delivery pipelines to identify vulnerable dependencies and insecure code before the release process progresses further.  By following this approach, security teams are less likely to discover large volumes of unresolved issues when development has been completed. AI-assisted development is becoming increasingly reliant upon dependency visibility.  Software bills of materials can provide a comprehensive inventory of components used across applications, while controlled package sources and dependency policies can prevent libraries with known vulnerabilities from entering the development environment. It is also necessary to clearly define who will be responsible for clearing this backlog.  Creating remediation deadlines based on risk level and assigning vulnerabilities to development teams responsible for the affected code can assist in making the process more measurable. Creating and verifying tickets as well as completing fixes automatically can also contribute to preventing vulnerabilities from remaining unresolved after they have been identified. The security backlog itself is not the only challenge that AI-assisted development can present when engineers approve code generated without understanding the design or underlying assumptions.  In the event that the generated code becomes part of critical application logic, such gaps in understanding can make it more difficult to make later changes and troubleshoot. Increasing volumes of artificial intelligence-assisted development are putting additional pressure on existing vulnerability management methods.  Generated code may bring in open-source libraries and transitive dependencies that may not be examined as thoroughly as manually selected components. These dependencies can result in a steady buildup of vulnerabilities that require assessment and remediation, which may cause security teams to face a steadily increasing backlog.  Counting CVEs alone cannot provide a reliable indication of actual risk. There may be more urgent urgency for issues with limited exposure when they affect application components, are exposed to untrusted input, or are associated with active exploitation. The business impact of an issue also plays a role in determining which findings should be addressed first, as well as which findings should be addressed first.  Security validation is therefore becoming increasingly integrated into the development process. Identifying insecure code and vulnerable dependencies before they reach production can be accomplished by integrating static application security testing and software composition analysis into development workflows and Continuous Integration/Continuous Delivery pipelines. The implementation of this approach can reduce the amount of remediation work that must be done by security teams following deployment.  Greater visibility into software components is also of importance. A software bill of materials can provide information about the open-source packages that are used across various applications, while controls around approved package sources can help prevent known vulnerable or unsuitable dependencies from entering development environments. Additionally, it is necessary to clearly define ownership of the remediation process itself.  By assigning vulnerabilities to teams responsible for the affected code and establishing a risk-based remediation timeline, and verifying the fixes, organizations can prevent unresolved findings from building up as AI-driven development continues. In order to ensure successful software development, organizations will need stronger visibility, risk-based vulnerability prioritization, and security controls throughout the development process. A strong focus on dependency management and remediation work can assist in preventing the growth of security debt from becoming a larger risk for the software supply chain.

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