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Why AI/ML in Cybersecurity Makes Sound Sense

Why AI/ML in Cybersecurity Makes Sound Sense

Amit Gupta, Solution Architect, Zensar

Artificial intelligence (AI) and machine learning (ML) have become critical technologies in information security. They emerged to help teams reduce breach risk and improve security posture efficiently and effectively. Today, as organizations struggle to safeguard themselves from the growing number of cyber threats, AI and ML technologies-based tools help them enhance their security posture.

Faster threat detection and incident response

AI/ML in security refers to tools and techniques that use artificial intelligence to autonomously identify vulnerabilities, respond to cyber threats, and reduce incident response time. Using sophisticated algorithms, AI/ML can spot cyber threats and malicious activities, detect malware, map cybercrime in real time, and bolster security infrastructure through pattern recognition. By comparison, manual methods delay understanding of attack patterns and increase the effort and time required by cyber analysts to act on and respond to threats.

However, advanced strategies to validate and analyze threats must have a defined workflow with powerful tools to lower costs and detect threats faster. AI and ML in cybersecurity help track hackers and creates new ways to detect cyber threats by learning different mixed patterns, finding vulnerabilities, making better risk assessments, and improving the overall security posture.

Though a compelling concept, AI cannot be set up and run independently. It needs to have specific data chunks on which decisions must be made. ML analyzes data from the past and presents optimum solutions for the present and the future. For this, past data needs to be made available to make the combination of ML, AI, and cybersecurity work.

Combining the strength of artificial intelligence and machine learning in cybersecurity with the skills of security professionals makes cyber defense highly effective —identifying the exact reduction in threat detection and false-alarm rate.

As with any powerful general-purpose, dual-use technology, there are significant challenges. While AI/ML can improve cybersecurity and defense measures, allowing for greater system robustness, resilience, and responsiveness, AI in the form of ML and deep learning can also intensify sophisticated cyberattacks that are faster, better targeted, and more destructive. Yet, the advantages outweigh these risks.

The driving forces that are boosting the use of AI/ML are -

  • speed of impact;
  • operational complexity; and
  • skills gaps in cybersecurity that remain an ongoing challenge.

Cybersecurity has been a primary concern for internet users and enterprises. With the help of artificial intelligence and machine learning, various malware and intrusions can be easily detected by setting up a security platform with a built-in mechanism for scanning vast amounts of data and data networks and recognizing all possible threats.

AI and ML are essential to secure the perimeters and redefine every aspect of cybersecurity today. These cover everything from anticipating and preventing breaches and protecting the proliferating number of threat surfaces to improving enterprise ability and scoring risks in a network.

So, is AI for cybersecurity on your priority list for this year?

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