The need for increased data security was recently put as a top priority on the global cybersecurity agenda by the EU General Data Protection Regulation (GDPR). This regulation imposes all companies having access to the personal data of the EU citizens to adopt more secure approaches to managing customer data, protecting against its accidental loss or illegal destruction, theft, and unauthorized disclosure. According to a number of cyber security reports, more than 50  percent of enterprises across the world have at least one incident of a major data breach or network attack annually. As more innovations in digital technologies end up in wrong hands, hacker attacks become more and more sophisticated and disastrous. That is why more companies rely on the AI/ML cybersecurity innovation. However, how can Machine Learning actually be leveraged to improve cybersecurity and data security, in particular? This article will explain the answer in details.

What Is Machine Learning and why it is so important?

Our partner IBM defines Machine Learning as a branch of Artificial Intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy. The machine learning process begins with observations or data, such as examples, direct experience or instruction. It looks for patterns in data so it can later make inferences based on the examples provided.

ML has proven valuable because it can solve problems at a speed and scale that cannot be duplicated by the human mind alone. With massive amounts of computational ability behind a single task or multiple specific tasks, machines can be trained to identify patterns in and relationships between input data and automate routine processes. Machine learning models are able to identify data security vulnerabilities before they can turn into breaches. By looking at past experiences, machine learning models can predict future high-risk activities so risk can be proactively mitigated.

Use of Machine Learning in Cyber Security

Cybersecurity is a set of technologies and processes designed to protect computers, networks, programs and data from attack, damage, or unauthorized access. In recent days, cybersecurity is undergoing massive shifts in technology and its operations in the context of computing, and data science is driving the change, where machine learning (ML), a core part of Artificial Intelligence can play a vital role to discover the insights from data.

Machine learning helps automate the process of finding, contextualizing, and triaging relevant data at any stage in the threat intelligence lifecycle. This could mean anything from finding dark web forum posts indicating a data breach, to detecting suspicious network activity in real time. To better understand previous cyber-attacks, and develop respective defence responses, ML can be leveraged in various domains within Cyber Security to enhance security processes, and make it easier for security analysts to quickly identify, prioritise, deal with and remediate new attacks.

The following points are just a few examples how Machine Learning can be used to aid security:

  • Automating Tasks

A great benefit of ML in cyber security is its capacity to automate repetitive and time-consuming tasks, such as triaging intelligence, malware analysis, network log analysis and vulnerability assessments. By incorporating ML into the security workflow, organisations can accomplish tasks faster, and act on and remediate threats at a rate that would not be possible with manual human capability alone. Automating repetitive processes means that clients can up or down scale easily, without changing the manpower needed, thus reducing costs in the process.

  • Threat Detection

Machine learning algorithms are used in applications to detect and respond to attacks. This can be achieved by analysing big data sets of security events and identifying patterns of malicious activities. ML works so that when similar events are detected, they are automatically dealt with by the trained ML model. In the case of security, by analysing millions of events, ML technologies learn to identify deviations from established norms. Instead of countering the latest threats after they have been identified like traditional systems do, ML can identify anomalies as they emerge. Against the background of quickly evolving threats, it’s not hard to see how valuable this is for protecting against data breaches and maintaining uptime and business continuity.

  • Increasing the speed of detection and response

AI and machine learning can easily analyze massive amounts of data in seconds, making it far faster than manually detecting threats. What’s more, they can implement patches and remediate threats in near real-time, dramatically improving response times. With the ability of today’s cyberattacks to quickly penetrate an organization’s infrastructure, razor fast detection and response is key to success.

  • Fraud Identification

Within the banking and finance industries, AI and ML models are being used as effective tools in identifying and preventing advanced attempts at fraud. Through predictive forecasting, models can build threat profiles to prevent fraud before it happens.

  • Provide endpoint malware protection

Algorithms can detect never-before-seen malware that is trying to run on endpoints.  It identifies new malicious files and activity based on the attributes and behaviors of known malware.

  • Protect data in the cloud

Machine learning can protect productivity by analyzing suspicious cloud app login activity, detecting location-based anomalies, and conducting IP reputation analysis to identify threats and risks in cloud apps and platforms.

  • Improving your overall security posture

With AI and machine learning, cybersecurity gets stronger over time as more data is analyzed and these technologies learn from past patterns to become more proficient at identifying suspicious activity. They also protect an organization’s infrastructure at both the macro and micro levels, creating more effective barriers than can be achieved using manual methods.

Cybersecurity is an essential consideration for any organization – especially as the world progresses digitally so fast. Cyber attacks are getting more sophisticated, requiring companies to up their game and respond in the same way. Whether preventing a future attack or analyzing why and how one happened in the past, using AI and ML models creates a faster, more comprehensive cybersecurity response.