The cybersecurity attacks are evolved from wild predator-prey chase to well-designed targeted attacks which are creating significant financial and reputational damages to businesses and government.

Identification of these attacks is becoming very difficult for the cybersecurity defense teams as the signs of these attacks are very subtle. Hacker are using disruptive technologies like Artificial Intelligence (AI) and machine learning to plan and launch these attacks.

The usage of these disruptive technologies in various areas of business are multiplying security vulnerabilities and escalating attack surface. At the same time, AI and machine learning can be used to automate security monitoring and predictive analysis.

Attack Detection – Lists, Patterns & Signatures

Identifying security attacks is all about monitoring organization’s IT infrastructure and compare with the patterns, white-lists, black-lists and signatures t0 detect the anomalies.

This traditional signature and pattern based identification and remediation is no more effective as the attack vectors are dynamic. The humongous data analysis and creating patterns for structured and unstructured data is a nightmare for the cybersecurity defense teams.

It is also important to note, time taken to identify anomalies makes a difference between defending attack and becoming prey (getting victimized).

“The thing people don’t get is that cybercrime is becoming automated and it is scaling exponentially”

By ,  Marc Goodman, a law enforcement agency adviser and the author of “Future Crimes.”

Automation

The attackers are designing state of the art techniques using AI and machine learning to footprint the organization’s cyber security controls and designing targeted attacks. This makes manual pattern-based analysis ineffective and subtle changes in behavior can be missed before they become patterns. This is where attackers get an edge over the security defence team.

To defend these targeted attacks, AI is used to analyze structured data quickly and can comprehensively read and learn unstructured data. AI can be trained the way your best security analyst monitors, analyze patterns and identify anomalies and replace them eventually. It can work tirelessly without getting distracted like human cybersecurity defense team.

Analysis can be performed by AI and identification of complex patterns can be done by machine learning in an exponential way. These capabilities needs be used to apply the deep learning from the previous data to improve the accuracy of identifying the normal behavior and the exception.

The combination of AI and machine learning can enhance cybersecurity defense team’s capabilities by leaps and bounds and solve many of the challenges CISOs are facing today

There are numerous advantages of this combination, top five are listed below:

• Time to identify respond subtle changes will reduce drastically
• Cyber security operations will take a leap from anticipatory to predictive
• Spending more time on prioritizing risks to help improve security posture
• Reduction in security team size
• Career advancement for security professionals in the regulatory compliance

AI will improve over a time but there will always need for intervention of security analyst for making the final decision about the incident.

Cybersecurity Vendors Using AI for Improved Solutions

IBM – Leading Managed Security Service Provider 

IBM Cognitive SOC platform puts cognitive technologies into security analyst’s hands, enhancing their ability to fill gaps in intelligence and act with speed and accuracy. IBM QRadar Advisor with Watson app brings cognitive capabilities to aid security analysts in their investigations and remediation through IBM’s QRadar security intelligence platform. The solution assists to investigate potential threats by correlating Watson’s natural language processing capabilities across security blogs, websites, research papers along with other sources, with threat intelligence and security incident data from QRadar, which can shorten cybersecurity investigations from weeks and days, to minutes

Symantec – Leading Cybersecurity Technology Vendor

Symantec’s latest edition of Endpoint protection suite is the first in the industry to fuses the Artificial Intelligence, advanced machine learning and memory exploit mitigation in a single agent, delivering a multi-layered solution to stop advanced threats.

Apart from the leaders and established cyber security organizations there are many startups coming up with predictive security based on AI and machine learning to name the few  Darktrace, Deep Instinct   PatternEx, Jask.io and Harvest.AI

Current Market Trends & Future Potential

AI is started its way in cyber security world to create a continuous loop of feedback between security analyst and security system which is “Active contextual modelling” to provide real time intelligence for attack detection.

Also as per Gartner out of top 10 strategic technology trends of 2017, the first trend is AI and machine learning.  Gartner predicts “AI and machine learning (ML), which include technologies such as deep learning, neural networks real-time-language processing, can also encompass more advanced systems that understand, learn, predict, adapt and potentially operate autonomously.”

Over To You

Artificial intelligence and machine learning sounds a great idea for cybersecurity defence. This will improve efficiency of identifying the anomalies, perform analysis and generate alerts.

Monitoring and analytics is just one piece of the puzzle. Given the current level of AI adaption in business, cyber security analyst will still play a significant role.

AI is forwarding its way in cybersecurity world and soon it will transform the way how cybersecurity defense works.