As Generative AI becomes more deeply integrated into our digital landscape, organizations face a growing need to manage application, technology, and cybersecurity risks effectively. The rapid evolution of AI technology amplifies the ease, potential, and complexity of cyberattacks. To better navigate this dynamic environment, organizations can adopt innovative approaches to prioritize risk management, optimize security and developer team collaboration, and improve performance metrics.
Risk Prioritization in the Face of AI
The proliferation of AI-driven applications and systems has led to an explosion of new security vulnerabilities. Common vulnerabilities and exposures (CVEs) have surged 500 percent in the past decade, making it increasingly challenging for organizations to manage and prioritize risks. Traditional methods of assessing vulnerabilities based solely on technical severity are no longer sufficient. Instead, taking a comprehensive approach that considers unique business contexts and real-time threat intelligence is essential.
Modern risk prioritization tools can provide organizations with a unified view of security findings, contextualized by their potential business impact. By normalizing the severity of findings across different security tools and assessing the business implications of affected assets, organizations can generate a single adaptive risk score. This approach allows security teams to focus on the most critical vulnerabilities first, optimizing remediation efforts and improving overall security posture.
To take it a step further, an AI-powered platform can ingest data from multiple security scanners/sources, normalize the findings and produce a prioritized list of risks based on business context and active threat intelligence. This method not only reduces time and resources spent on low-priority issues but also enhances security effectiveness by targeting the vulnerabilities that pose the greatest risk to the organization.
Leveraging AI for Enhanced Security
AI itself plays a key role in improving cybersecurity risk management. AI-driven platforms can analyze vast amounts of data from diverse sources to uncover trends and issues, and provide deeper insights and more accurate threat detection. Machine learning algorithms and natural language processing can also enable these platforms to correlate findings from different security tools, providing a more holistic view of the security landscape than using a single solution without AI.
One of the other key benefits of AI-powered application security solutions is their ability to reduce duplicate findings and false positives across various scanners. For example, the same vulnerability might be reported by both static application security testing (SAST) and dynamic application security testing (DAST) tools. AI can correlate these findings, eliminate redundancy and streamline the remediation process. This capability not only reduces the workload for security and development teams but also accelerates the mean time to remediation (MTTR).
Moreover, AI enhances the precision of vulnerability assessments. By integrating pre-production and runtime analysis, AI-powered platforms can provide strong signals about the real impact of identified issues. This enables security teams to address the root causes of vulnerabilities more efficiently, improving the speed and accuracy of remediation efforts.
Collaboration Between Security and Development Teams
Effective cybersecurity risk management also requires seamless collaboration between security and development teams. AI-powered platforms facilitate this collaboration by providing a unified view of risks and remediation priorities. This shared perspective helps both teams to align their efforts and focus on what matters most.
One of the significant challenges in application security is the disconnect between security findings and their resolution. Developers often receive numerous security alerts without clear guidance on prioritization, leading to inefficient remediation processes and delayed software releases. AI-powered platforms bridge this gap by correlating security findings with development workflows, ensuring that appropriate issues are promptly routed to the appropriate teams.
Additionally, remediation workflows should be automated based on risk scores. By automating routine tasks and providing actionable insights, AI-powered solutions can enhance security team productivity and enable faster, more secure software releases. This collaborative approach not only improves security outcomes but also fosters a culture of shared responsibility and continuous improvement.
Holistic Governance Layer Ac ross Risks
To protect against threats, CISOs need a comprehensive governance layer to see across their full scope of risk, including perspective on all tools and teams from developers to cloud security. As AI continues to reshape the digital landscape, managing cybersecurity risk will be more complex and critical than ever.
Furthermore, proper governance helps determine the ideal time to change an organization’s scanners or shift security sources by keeping their efficacy more transparent. When that governance layer is overseeing a security approach based on risk, it is able to provide the modularity necessary to keep programs effective.
The ability to identify, prioritize, and remediate the highest-risk vulnerabilities efficiently is essential. AI-powered platforms offer a new model for cybersecurity risk management, empowering organizations to stay ahead of emerging threats and maximize the ROI of their security investments. Organizations should prioritize their greatest business risks, use AI to enhance their security, and foster collaboration between security and development teams. By doing so, they can navigate the challenges of the AI era and improve their risk and security posture.
About the Author
Karthik Swarnam is Chief Security and Trust Officer at ArmorCode. He is a cybersecurity leader with over 25 years of experience, including former CISO roles with Kroger, DIRECTV, and TransUnion. Karthik can be reached on LinkedIn and more information can be found on ArmorCode’s website: https://www.armorcode.com/
Source: www.cyberdefensemagazine.com