AI: A deep dive into detection, prevention, and adaptability

The use of AI in cybersecurity is rapidly evolving, with multiple use cases transforming into reality. While these advancements show promise, the scalability and accuracy of AI solutions in large enterprise environments are areas that require collective evaluation for a transformative impact on the digital security landscape.

As the digital landscape continues to evolve, the role of Artificial Intelligence (AI) in cybersecurity becomes increasingly crucial. Pradeep Yadlapati, Senior Vice President at Innova Solutions, provides insights into the pillars of responsible AI in the context of early detection, prevention of cyber threats, and the adaptability of AI systems in the face of evolving challenges.

Early Detection and Prevention of Cyber Threats

In the modern digital landscape, AI stands as an indispensable sentinel in the realm of cybersecurity. It excels in the early detection and prevention of cyber threats through an array of sophisticated mechanisms. AI systems meticulously monitor network traffic, user behaviour, and system activities, establishing a baseline for what constitutes normal operations. This enables them to swiftly flag any deviations or anomalies, signalling potential threats in real-time.

Through a combination of pattern recognition, machine learning, and real-time analysis, AI exhibits the ability to discern both known and unknown threats. Be it the identification of malicious code, malware, or unusual login patterns, AI algorithms enable swift action, thereby permitting organizations to respond proactively. In essence, AI serves as the vigilant gatekeeper that identifies, isolates, and mitigates potential threats before they manifest into full-blown security incidents.

Real-World Scenarios of AI Identifying and Mitigating Vulnerabilities

In the world of cybersecurity, AI has shown its effectiveness in finding and contexualizing vulnerabilities based on the risk, exploits in the wild and the potential business impact based on existing system weaknesses primarily driven through End of Life/End of Support infrastructure. AI-based security tools are adept at ferreting out the elusive zero-day vulnerabilities that often pose the most significant threats. By meticulously analyzing system behaviour and identifying irregular activities or patterns, AI plays a pivotal role in discovering these vulnerabilities.

Moreover, through AI-driven penetration testing, organizations are empowered to simulate and scrutinize potential attack vectors, thereby fortifying their defences against existing vulnerabilities before threat actors can exploit them. There is a growing trend of using AI in the DevSecOps model to improve the “Shift-Left” concept by implementing secure coding principles at the code creation level thereby reducing the cost of fixing vulnerabilities in the production environment.

Many AI tools in the market provides advanced software composition analysis (SCA) helping developers find, prioritize, and fix security vulnerabilities and license issues in open-source dependencies across your IDE or CLI, scan pull requests before code merge, scan CI/CD pipeline etc supporting Shift-Left approach.

Malware detection is another exemplary facet of AI's capabilities. It is primed to spot new strains of malware and zero-day threats by meticulously scrutinizing file behaviour, code execution, and network traffic. This capability not only helps in the detection but also in the swift mitigation of threats before significant damage can be wrought.

Another example where using AI based Predictive Analysis can help analysing historical data by recognizing patterns, trends and emerging threats to proactively predict future cyberattacks, there by address vulnerabilities and strengthen their defenses.

Further, Integrating SIEM (Security Information and Event Management) and UEBA (User and Entity Behaviour Analytics), organizations can achieve a more comprehensive and proactive security posture. SIEM provides event correlation, log management, and real-time monitoring, while UEBA enhances threat detection by focusing on user and entity behaviour analysis to identify unusual activities for detecting insider threats, credential theft and compromised accounts.

NLP-driven AI is helping companies in monitoring potential threats originating from public discussions or internal channels. It analyses text data to identify cyber threats in chat logs, social media, and other forms of communication.

We continue to see significant vulnerabilities due to misconfigurations through human errors and lack of knowledge by information security professionals that hinder ability to reduce the risk exposure. This challenge is now being addressed through the use of AI where configuration changes are baselined and checked in near real time to identify and find out the open vulnerabilities thereby reducing risk exposure considerably.

Adaptability of AI Systems in the Face of New Challenges

In an ever-evolving landscape of cyber threats, AI systems exhibit a remarkable degree of adaptability that ensures their continued effectiveness as guardians of digital security.

Continuous learning is at the heart of their resilience. Leveraging sophisticated Machine Learning algorithms, AI systems absorb new data, learning and adapting from emerging threats. This equips them with the ability to recognize novel attack vectors and adjust their threat detection models to accommodate the ever-shifting threat landscape.

AI systems can focus on behavior-based detection rather than relying solely on known patterns or signatures of threats. By understanding normal user and network behavior, AI can better detect anomalies that may indicate new, previously unseen threats.

Collaboration between AI and CyberSEC professionals called as "augmented intelligence” is important to bring in the human expertise which can provide greater insights into the context of threats which AI alone may struggle.

AI can be used to enhance authentication systems, implementing adaptive authentication that adjusts security measures based on the perceived risk level. For example, it may prompt for additional authentication steps when detecting suspicious behavior.

Further, the integration of threat intelligence is paramount. AI systems remain interconnected with external sources and up-to-date information feeds. This enables them to stay abreast of the latest threats, tactics, and vulnerabilities. Their proactive response mechanisms evolve based on real-time threat intelligence, positioning them at the forefront of defense against rapidly changing threats.

Autonomous response is another noteworthy facet of AI's prowess. These systems can autonomously respond to threats by isolating compromised systems, blocking malicious activities, and applying patches and updates to address vulnerabilities. Their capacity to detect anomalies and emerging threats stands as a testament to their ongoing relevance in a cybersecurity environment that never ceases to transform.

The ability to use AI at scale is now helping us internally at Innova Solutions to analyse failed logins which is providing a wealth of information on the behaviour attack pattern and the Tactics, Techniques and Procedures being used to gain access of our employees. Armed with this information we are utilizing playbooks in our Security Orchestration and Automation (SOAR) tools to take corrective action based on the degree of confidence that we develop over a time.

We are actively evaluating AI based techniques to trend every employee behaviour across the organization through various log sources to understand their historical logon patterns, geolocation, application accessed and various indicators typically captured though the use of User Entity Behaviour Analytics (UEBA) solution and this data is corelated with employees travel plans, recent HR information feed and role within the organization to increasingly reduce false positives and eliminate “Alert Fatigue” for Cybersecurity analysts.

In summary we are seeing an evolving trend of AI usage in the cybersecurity field which is evolving at a rapid pace and there are multiple use cases which are transforming into reality. However,these are still early days of adoption and we have to collectively evaluate the ability of these solutions to scale up for large enterprise environments with 100% accuracy, which will truly transform the digital security landscape.

Pradeep Yadlapati, Senior Vice President at Innova Solutions