CIOInsider India Magazine

Parag is a risk management professional with a keen interest in cybersecurity, AI, operational risk, and fraud risk management. He has over 25 years of experience in multinational companies across different industries and has held leadership roles like Chief Risk Officer and Chief Information Security Officer. He also plays a critical role as a key member of the advisory council at CyberSec India Expo.

Historically, in the cybersecurity world, threat actors have often been two steps ahead of the defenders. With the recent advent of open-source tools like Machine Learning (ML) and Artificial Intelligence (AI), the attackers seem to have now taken a significant lead. Cybercriminals have leveraged Generative AI to pivot the sophistication, speed, and scale of attack vectors exponentially. Traditional security measures cannot match the continuously evolving onslaught of attacks, but the use of AI in cybersecurity applications can be an inflection point in how we tackle cybersecurity challenges.

The Growing Complexity of Cyber Threats
As the world is moving towards hyper-connectivity with cloud, APIs, and Internet-of-everything (IoE), the threat actors are getting the advantage of an ever-expanding attack surface. The sheer number of vulnerabilities and misconfigurations across these interconnected systems is the biggest challenge faced by security professionals, as remediation becomes an impossible task without impacting business operations. Additionally, detecting incidents, weeding out false positives, and investigating and mitigating real attacks are taking a toll on security professionals. With the fast adoption rate of AI technology in business applications, the number of AI-based threats is adding fuel to the fire.

Another key driver of this complexity is the increase in state-sponsored attacks and organized cybercriminal groups, which have substantial resources at their disposal and are investing in highly advanced technologies. AI enables the automation of attacks and provides greater scale and precision, enhancing adversarial capabilities. AI-based tools like Worm-GPT and Fraud-GPT, available on the Dark-Web for a fee, are leveraged by the attackers to develop new techniques to attack and evade detection at an unprecedented scale.

How AI Enhances Threat Detection
Detection of threats involves analyzing vast amounts of data and alerts from a large number of systems, correlating these to identify patterns, weeding out false positives, and quickly deploying measures to block malicious activity. While traditional rule-based tools and simple automation have reduced manual efforts, AI can significantly bolster this effort with its ability to parse huge datasets in real-time. AI-based tools can empower security professionals in areas including pattern analysis, threat identification and controls analysis, actionable insights, and autonomous mitigation measures.

Machine learning algorithms and AI-based tools can be trained on historical data to identify new threats like zero-day vulnerabilities. AI can monitor network traffic in real-time, correlate alerts from different systems and identify patterns that may indicate an active threat. AI is adept at analyzing patterns and pinpointing threats like a needle in a haystack, much more effectively than humans. AI systems can also ascertain the effectiveness of existing controls, and guide security professionals on appropriate remediation actions required.

Additionally, AI-powered systems can learn and evolve continuously to improve their detection capabilities and make autonomous decisions instantly to block threats with minimal or no human intervention. AI-based threat intelligence tools can also help in analyzing unstructured data from social media, the dark web, and other sources.

Benefits of AI in Cybersecurity
Cybersecurity professionals have realized the vast potential of incorporating AI in traditional cybersecurity products, tools, and processes to improve efficacy and reduce fatigue. Today, every cybersecurity product and tool has integrated or is on the path to integrating AI and ML technology. This integration of AI into cybersecurity offers several advantages:

Proactive Defense: By identifying patterns and predicting potential threats, AI enables a proactive approach to cybersecurity, preventing attacks before they occur or detecting them in real-time.

Speed and Efficiency: AI can process and analyze data at a speed unmatchable by human analysts, enabling instantaneous identification and response to threats.

Scalability: AI technology can handle huge volumes of data across multiple networks and systems. AI systems can scale up to handle the ever-increasing data load without compromising on performance.

Minimize Human Error: Automated threat detection using AI minimizes the risk of oversight inherent in manual processes and errors caused by fatigue.

Accurate Detection: AI technology can analyze large datasets and correlate events to identify patterns that may indicate a threat. This reduces the number of false positives and ensures that genuine threats are not overlooked.

Real-Time Response: AI-powered tools can respond to threats in real time, automatically isolating affected systems, blocking malicious traffic, or initiating other defensive measures. This instant response is critical in minimizing the impact of an attack.

Defensive measures: AI can be used to reverse engineer exploits, helping security engineers to develop and deploy patches and signatures much faster.