A seasoned professional having 21 years of industry experience in IT Management (IT Infrastructure, Information Security, Digital Transformation, Business IT Operations, Business Process Improvement and innovations) for various industry verticals like ITeS, FnB, Retails, QSR, Automotive, and more.

CIO Insider Magazine recently engaged in an insightful conversation with Arvind Kumar, wherein he shared his insights about the Implementation of Artificial Intelligence (AI) Solutions for Cyber security. Below are the excerpts from the exclusive interview –

The Crux of AI
In this technology-integrated era, AI influences almost every aspect of our lives and dynamically proliferates change across a wide range of industries. Also, given technology’s reach and business growth, as data is abundant and its security is significant due to its sensitivity and exclusiveness, cyber security through AI solution implementation proves crucial for organizations today. AI learning the evolution patterns prolifically enhances IT security performance at an enterprise level by providing detailed analysis and threat identification. This leads to helping security professionals to avoid data breach risk and prioritize risks, quick or efficient incident response, and may help to identify cyber-attacks even before they occur. We need to understand that offensive AI based smart attacks can’t be handled by human but can be prevented by defensive AI solution.

The Key to AI Implementation in Cyber Security
The evolution of AI-driven cyber security solutions involves many key factors just to ensure they are effective and reliable. Accuracy of threat detection, response, and mitigation efficiently also quick to reduce MTTD and MTTR. Transparency is one of the key factors when it comes to the integration and capability of AI solutions, it is crucial to understand how AI solution conclude threat detection and on what basis it provides the recommendations, along with compatibility of integration to different systems, platforms, and protocols to ensure seamless implementation with other third-party systems and solutions available within organization. It also depends on how AI-based solution is trained, particularly on various threat detections like bias-free training data reduces the risk of inaccurate results and provides better threat detection. From compliance

perspective, an AI solution must be transparent in how they store, process, and deliver data and what are all compliances adopted w.r.t the region/country/the local authorities. From user experience prospective it should be intuitive, easy to configure, and provide clear and actionable insights for security teams to respond effectively. Last but not least is the return on the investment (not only just cost but effectiveness as well), by reduction in security incidents, operational efficiency gains, and potential long term cost savings compared to the pre-existing solutions.

Strategies that Propel the Growth Scale
Organizations need to establish a robust process to ensure that AI based cybersecurity remain continuously updated and resilient against emerging and new threat vectors. This ensures that the AI system remains protected against all known vulnerabilities. However, a robust change management process for testing compatibility and resiliency is a must. Feedback loop for AI training needs to be enabled by encouraging users to report false positives, false negatives, and other anomalies, can help to improve accuracy and reduce errors; the effectiveness of AI model can be measured by regular security audits. If an AI model is getting trained on a regular basis with latest global cyber threats and vulnerabilities intel feeds including attack patterns, user, device and application/service behaviors etc. promptly and proactively, then keeping cyber security continuously updated and resilient against emerging threats is possible.