By Vinod Shah, CIO/CISO of Bank (Article No. 3 of 3):
There is no denying the fact that the continued evolution in AI will redraw the landscape of cybersecurity. In this respect, the future of digital defences rest on how effectively AI capabilities are balanced with human expertise.
Symbiosis of AI & Human Intelligence
1. Complementary Strengths: While AI can handle large volumes of data and identifying patterns, human intuition brings creativity, contextual understanding, and ethical judgment.
2. Augmented Intelligence: AI is not intended to replace human professionals but to augment human decision-making. Hence, making AI as an ally is the future.
3. Continuous Learning: AI systems and expert humans have to continuously learn to match the dynamics of evolving threats. It must be well understood that AI will learn from the DATA available, and hence the DATA being fed into the AI system development has to be vetted and needs to be without any bias.
AI Enhances Threat Detection & Response
AI is be able to analyze security logs and network traffic in real time, alerting human analysts to possible threats. Further, machine learning algorithms can perform triaging of security incidents, wherein human experts need to look into only the most critical issues. Lastly, AI can give predictions on attack vectors, hence enabling proactive defence strategies devised by human security teams.
Human-Led Strategy & Oversight
All AI can do is provide data-informed insights, but it has to fall to human judgment to assess the overall risk and establish security priorities. Hence, security policy and governance frameworks that guide AI implementation should be driven by human expertise. Human judgment will continue to play a key role in navigating the ethical implications associated with using AI in Cyber security, including privacy concerns and potential biases.
Advanced Threat Hunting
Machine learning can analyze large data volumes in detail to identify subtle signs of compromise. Security analysts should be enabled to drive AI systems based on their judgment, research, and investigation of hunches and anomalies not picked up by AI. Incorporating AI analysis into human-curated threat intelligence will potentially result in a more complete view of the threat landscape.
Incident Response & Forensics
AI can automatically initiate a response to immediately contain a perceived threat. Deep investigations by security experts unravel root causes and the full extent of breaches. Machine learning assists in processing large volumes of forensic data, thereby speeding up human-led investigations.
SOC of the Future
Monitoring of network activities and security alerts is done on a continuous basis using AI. SOC analysts oversee the operations of AI, intervene when needed, and make high-order decisions. Integrated systems that allow for the smooth interaction and collaboration of AI tools with human operators.
Adaptive Security Architecture
Adopt self-learning security systems that grow with emerging threats. Security architects should oversee the growth of such adaptive systems based on emerging trends and organizational needs. Do regular assessment of security posture through AI analytics, coupled with human expertise in security audits.
Cyber security Workforce Development & Specialization:
AI literacy refers to the training and exercising of security professionals for effectiveness in the use and communication with AI. There is a dire need to create new positions around AI and Cyber security.
Soft Skills: In this era of cybersecurity, emphasis is needed to be placed on uniquely human soft skills such as critical thinking, communication, and leadership.
AI in automated Regulatory compliance checks: Automated systems may monitor compliance continually and report on any discrepancy against the various regulatory standards set. However legal and compliance experts have to explain the complexity of regulation and configure the AI systems correctly. A variety of regulatory requirements necessitates the explanation of decisions made by the AI system, thereby building trust in AI.
Challenges & Considerations
Trust in AI: Creation of trust in AI-driven security decisions for all kinds of stakeholders.
Data Quality: Ensuring that data applied to training AI systems is accurate, complete, and of high quality.
Skill Gap: How to make up for the shortage of experts with this dual skill in Cyber security and AI.
Adversarial AI: Attack techniques that can intelligently react to remain undetected.
Conclusion
The future of Cyber security is about how the world effectively marries the two strong forces of AI & Human expertise One could achieve far more resilient, adaptive, and intelligent Cyber security approaches by developing a symbiotic relationship between AI systems and human professionals. The best Cyber security strategies will be those that find the right balance between AI and human expertise – the whole being far greater than the sum of its parts.
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