Preventing data breaches with AI/ML for real-time threat mitigation

It is safe to say that Artificial Intelligence is an indispensable part of most organization’s digital transformation roadmaps, with a report by IDC projecting that the AI market in India alone will grow to $7.8 billion by 2025. Among the critical areas where AI will have a significant impact is the domain of cybersecurity.

Amidst an ever-evolving threat landscape, conventional approaches to cybersecurity – encompassing measures such as intruder detection systems, firewalls, and resource-intensive analysis – are proving insufficient in the face of highly sophisticated cyber-attacks. In this context, leveraging AI/ML technologies empowers businesses with superior insights into the threat landscape, enhanced productivity, and greater economies of scale. This enables them to stay ahead of the cybersecurity curve.

Bolstering Cybersecurity and Threat Mitigation

Consider how cybersecurity has traditionally been handled across most enterprises. Cybersecurity teams relied on signature-based detection systems that compared incoming traffic to databases of known threats. In the event of a match, an alert would be triggered in the system, leading to action to block or quarantine the identified threat.  

However, the rigidity of these systems proved to be a significant challenge. On one hand, a large number of false positives were generated due to legitimate traffic being flagged if it shared characteristics with a known threat. On the other hand, new or previously unidentified threats were able to slip by undetected. Compounding these challenges was the reliance on manual analysis by security personnel to investigate logs and identify potential breaches. These processes were both time-consuming and prone to human error, even more so when teams were stretched due to a shortage of talent.

Contrast this with the new security status quo ushered in by AI-driven systems. Inflexible signature detection is replaced by ML algorithms – trained using vast datasets including live intelligence feeds, historical data, user activity, and network traffic – that continuously learn and adapt to a dynamic threat environment. They can identify hitherto unseen patterns and signs of compromise that are indicative of a data breach and take immediate action in real time for prevention or mitigation. By constantly updating themselves based on historical data, they anticipate potential vulnerabilities and threats with a high degree of accuracy, bolstering defenses and thwarting attacks long before they appear on the horizon.

Furthermore, automated threat response greatly reduces the dependence on overburdened cybersecurity teams. Instead of human analysts having to manually investigate potential breaches and hope to take action in time, AI automation acts immediately against active threats, minimizing the impact of breaches and safeguarding sensitive data from unauthorized access and exfiltration. This is a tremendous force multiplier for organizations, vastly expanding their capacity to handle sophisticated threats and reducing the scope for human error. The AI ensures that no threat evades detection, and no legitimate activity is disrupted due to false positives.

By analyzing the content and structure of email and other communications, AI/ML solutions can foil attempts at social engineering and phishing. It can conduct user behavior analysis at scale and promptly flag suspicious or risky activity by either careless or malicious users. Moreover, by facilitating automated threat response, it enhances businesses' security postures, ensuring that systems are constantly fortified with the latest patches.

Towards a New Age of AI-Driven Cybersecurity

In the recent past, Indian private and public sector companies alike have been wracked by sophisticated and brazen cyber-attacks – with one of the most alarming recent examples being the theft of personally identifiable information of 815 million Indian citizens, via an alleged data breach at ICMR (Indian Council for Medical Research). Such incidents bring into stark relief the critical need for organizations across the country to evolve their cyber-defense strategies.

AI has already shown itself to be a key piece of the cybersecurity puzzle - automating and streamlining processes, enhancing the accuracy and efficiency of threat detection and response, reducing manual effort by human cybersecurity professionals, and reducing costs over time. But we’ve only scratched the surface of the vast potential of the technology in the security sphere. In the years ahead, businesses can expect to benefit from even more sophisticated adaptive security systems, backed up by AI-driven smart learning. Analysis of simulated attacks will vastly enhance the AI’s real-world detection and response capabilities.

It is crucial to keep in mind however that AI cannot serve as a wholesale replacement of human cybersecurity expertise. The intuition, experience, contextual knowledge, and critical thinking of human experts are crucial to validating the output of automated security systems and ensuring optimal outcomes. AI can serve as a valuable learning & development tool for cybersecurity personnel. This ensures that they are equipped with critical intelligence on attack patterns and threat vectors based on previous data breaches that a company would have faced.  

Building a comprehensive understanding of the capabilities and limitations of AI in cybersecurity among the workforce will enable enterprises to facilitate seamless AI-human collaboration. Human ingenuity, coupled with the sheer scale and scope of AI’s analytical and learning abilities, will prove to be the ultimate defender for organizations in our new digital epoch.

Vaibhav Tare

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Vaibhav Tare is VP & Chief Information Security Officer (CISO) of Fulcrum Digital