In today’s world of digitization, enterprises are highly challenged to keep their business-sensitive data protected from internal and external threats. With an interconnected business ecosystem, enterprise digital boundaries are stretching beyond their direct control limits.

As threats from malicious actors increase, cyber security & resilience becomes crucial. This necessitates capabilities beyond mere protection, including detection, response, and recovery from cyber-attacks & breaches to ensure business continuity. The existing security stack lacks scalability to handle the volume of data and agility to respond to continuously emerging security risks. Artificial Intelligence (AI), and more specifically Generative AI (GenAI), can transform cyber operations by mimicking cognitive processes while handling huge volumes of cloud data without delaying cyber threat detection and response.

The rise of AI in cybersecurity

According to Forrester, “Using AI tools as part of the analyst and security team’s workflow and with descriptive datasets will be far more powerful.” As the sophistication in threats like ransomware, DDoS, phishing, advanced persistent threats (APTs), insider attacks, or zero-day exploits continues to rise, AI-powered threat detection systems can identify and investigate cyber threats with extreme velocity and precision, processing high volumes of data in real time.

Real-time threat detection and analysis

As the IT landscape on the cloud evolves and extends beyond traditional boundaries, AI identifies anomalies and inconsistencies in user behaviors and traffic patterns by analyzing logs from infrastructure, applications, and cloud networks. The advantage of AI lies in its ability to detect both known and unknown threats with enhanced fidelity. Empowering cybersecurity analysts with AI enhances their effectiveness in analysis and reduces the mean time to detect incidents.

For instance, AI/ML-based predictive analysis for phishing and targeted users enables organizations to forecast high-risk users who have been targeted and identify the effectiveness of their controls. By deploying predictive models based on data science techniques like pattern analysis and trend analysis, organizations can better anticipate and mitigate potential threats.

Enhanced response and remediation

AI in cyber detection goes beyond anomaly detection and malware analysis, excelling in predictive modeling for cyberattacks and better vulnerability management through prioritization of remedies based on risk exposure. For example, using a predictive analytics model to understand a threat actor’s modus operandi can help develop strategic defenses.

AI also improves cybersecurity across various fronts, such as controlling access based on behavior analysis, optimizing network segmentation to prevent lateral movement, phishing protection, and intelligent security orchestration for autonomous security. Leveraging AI to respond to cyber threats and remediate across the enterprise cloud fabric helps mitigate sophisticated cyber threats with better incident scoring, automated triaging, rapid automated containment, and effective collaboration with human security experts.

Streamlined recovery with GenAI

In the recovery process, generative AI can perform damage analysis and adapt recovery workflows based on received signals. Post-recovery, AI can further validate system & data integrity, carry out compliance checks, and assure full recovery while being vigilant during the hyper-care period to prevent incident recurrence.
Security experts benefit significantly from these additional capabilities, making their roles more effective and impactful. Running simulation attacks with generative AI can further hone the skills of security experts and drive improvements in the process.

Addressing the limitations of AI

While AI can propel cybersecurity to new heights, it has its limitations that can influence the quality of cyber detection and response. One critical limitaton is AI’s susceptibility to altered input data, which can induce misclassifications or evade cyber detection against its original goal. Ineffective or biased preparation of input data for AI models can significantly thwart AI’s outcomes. Therefore, before mainstreaming any transformative AI usage, it is vital to have well-defined development and validation processes, especially concerning the data sets used.
Continuous carefulness and evaluation of AI usage are required, particularly when it involves direct impacts on humans and society at large.

Empowering your cybersecurity infrastructure with AI is not just an upgrade; it’s a necessity in today’s digital age. By integrating AI-powered threat detection and response systems, organizations can enhance their resilience against evolving cyber threats, ensuring robust protection, swift response, and efficient recovery. Join the future of cybersecurity—embrace AI and secure the cloud.

The author is Madhan Raj J, Associate Vice President, Cobalt Cloud Solution Strategist, Infosys.

Disclaimer: The views expressed are solely of the author and ETCIO does not necessarily subscribe to it. ETCIO shall not be responsible for any damage caused to any person/organization directly or indirectly.