Five worthy reads is a regular column on five noteworthy items we’ve discovered while researching trending and timeless topics. This week, we discuss the escalating significance of AI and machine learning in IT security space.

Illustration: Dorathe Victor

One of an IT team’s biggest challenges is making sense of the mammoth amount of data that corporate infrastructures produce and consume while identifying and responding to real-time cyberattacks. According to research conducted by the Ponemon Institute in its 2019 Cost of a Data Breach Report, a cyberattack has an average lifecycle of 314 days from its launch to containment. This means detection and mitigation of the risks of a cyberattack are painfully time-consuming.

Thankfully, artificial intelligence (AI) and machine learning (ML) have become an invaluable part of the IT security space. A recent study by Capgemini Research Institute reveals that 69 percent of senior executive respondents indicated they would be unable to thwart a cyberattack without AI. The same study suggests that two-thirds of organizations have planned to include AI in their IT security strategy by 2020.

AI and ML are increasingly crucial in orchestrating incident-response automation. This incident-response workflow automation contributes significantly to zero-day threat detection, and can drastically reduce cyberattack response times to milliseconds.

Here are five interesting reads from across the internet that shed light on the growing demand and adoption of AI for IT security: 

1. How AI, ML, and automation can improve cybersecurity protection
Since AI and ML are not only used in cybersecurity but also in cybercrime, the bad guys use them to better profile their victims and accelerate attacks. The same technology that can be used in fraud detection can also be used to overcome the existing security measures.

2. Cybersecurity strides: AI and machine learning aren’t the ultimate cybersecurity weapons … yet
AI can enable machines to perform a much broader and more complex array of tasks than was possible in the past.

3. The Promise and Challenges of AI and Machine Learning for Cybersecurity
Today, AI and ML technologies are in the spotlight for many industries. Cybersecurity is an important beneficiary of these new technologies, but organizations need to have a solid understanding of how ML-based algorithms work, and how they can enhance security to benefit in the future. 

4. AI and machine learning in cybersecurity: Trends to watch
With organizations processing escalating volumes of data in the cloud, cybersecurity strategies need to be honed with next-generation, self-learning technologies that can provide sophisticated levels of automation and boost threat-protection capabilities.

5. When Every Attack Is a Zero Day
Stopping malware the first time is an ideal that has remained tantalizingly out of reach. But automation, artificial intelligence, and deep learning are poised to change that. Recent advancements in processing have increased these capabilities, and the costs of the underlying tech have decreased, putting deep learning applications within the reach of many industries—including cybersecurity.

The reality is that we’re only beginning to grasp how AI and ML can help build enterprises of the future. Enterprises require smarter IT security workflows to identify and preempt incidents in real time. This includes understanding the past and proactively anticipating future events, and that entails cognitive processing—using AI algorithms to extract meaningful relationships, patterns, and concepts from data.

With a plethora of options available today, the first step towards this change is to deploy AI-powered solutions that will provide intuitively actionable insights for a solid, self-learning IT security infrastructure.

What is your take on this? Let us know in the comments section below.

 

Srilekha Veena Sankaran
Marketing Analyst

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