AI for Smarter Cybersecurity

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AI for Smarter Cybersecurity

With global information security expenses expected to reach $170 billion by 2022, all eyes are on the cybersecurity industry to innovate more effective, resilient mechanisms and tools.

The cyberattack on enterprises has become a massive threat, it’s something that keeps growing and evolving at a rapid pace. The attack surface of the enterprise is the net total of the points where attacks have a probability to occur. At this surface, the attacker or an ‘unauthorized user’ can try to manipulate or extract data using a myriad of techniques. The question arises here as to how is the involvement of AI in smarter cybersecurity helps?

Depending on the size of the enterprise, there exist ’n’ number of time-varying signals that are to be analyzed in order to calculate risks in an accurate manner. As a result, the problem becomes quite a complex task for humans alone to solve. This is the main reason why improving cybersecurity then falls into the hands of AI and machine learning. Such a situation though unparalleled is being tackled with the help of AI-based tools and resources, an emerging technology that is yet to play an important role in cybersecurity.

Artificial intelligence has the capability to analyze and run huge amounts of data in an iota of seconds which we can refer to as moving with the speed of light. What does this mean? Precisely, cyber threats can now be detected in real-time with a predictive risk model using Machine learning. Let’s delve deeper into this.

The Three A’s of AI

  • Assisted: Intelligence that is already in use and is widely available. Helps organizations to improve their effectiveness and processes.
  • Augmented: The ever-emerging intelligence, which enables people and institutions to do things that are otherwise tedious for human beings.
  • Autonomous: The future intelligence, developed for a better tomorrow. AI machines that act in their accordance like self-driving vehicles, etc.

Applying AI to Cybersecurity

Companies are becoming smarter every day as the threat that cyberattacks pose to any firm remains one of the biggest. In fact, firms have to work even harder to protect their clients as well as their assets which demands beyond just automating reactive measures. Putting AI and machine learning to good use is about letting IT professionals work towards proactive threat detection to preemptively avoid or thwart threats.

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At the same level, cybersecurity throws light on some of the unique issues:

  1. A vast attack surface
  2. 10s or 100s of thousands of devices per organization
  3. Hundreds of attack vectors
  4. Shortfalls in the number of skilled security professionals
  5. Masses of data that have moved beyond a human-scale problem

In order to overcome these challenges, AI and machine learning-based cybersecurity systems are being trained in a continuous manner. The data is used across all the enterprise information systems is gathered and analyzed. This is then used to form a pattern across millions of signals relevant to the enterprise attack surfaces.

Henceforth, an improvised and par-level intelligence system is derived helping human teams across cybersecurity to work and think towards an even better solution.

  • Predictive Threat Detection: Trends and daily updates are useful data in anyone’s day-to-day life including attackers too. AI-based cybersecurity tools help provide the latest industry-specific information to make it easy for detecting what could be a possible means of threat. And not just this but what is more likely to cause a threat to the organization.
  • Incident response: AI-powered systems can provide improved context for prioritization and response to security alerts, for fast response to incidents, and to surface root causes in order to mitigate vulnerabilities and avoid future issues.
  • Effectiveness: Just employing security tools is not enough, keeping in check that it is maintaining the right posture throughout is equally important. AI helps manage the same by letting the team know about the information security shortfall and gaps so it can be made better.
  • IT Asset Inventory: A hold of complete and accurate information on the devices, users, and software within the organization that has appropriate access to the systems is critical.

Conclusively, AI and machine learning are now considered to be inevitable in the field of cybersecurity. Any organization with an IT team using AI-based cybersecurity systems can be said to have been maintaining good security practices. This doesn’t just shrink the probability of the cyberattack but also reduces the chances of constantly chasing after the malicious activities. Since humans can no longer scale to adequately protect the dynamic enterprise attack surface, AI provides much-needed analysis and threat identification that can be acted upon by cybersecurity professionals to reduce breach risk and improve security posture.


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