ML and Analytics in Proactive Cybersecurity: What to Expect

 

Business operation today are largely being carried out through hybrid or remote work models. Though this situation might change soon, it is hard to predict exactly when it will completely come to an end. As a result, the number and complexity of cyber-attacks are increasing. Some, like the Nvidia employee credentials leak, the cyberattack on the Costa Rican Government and the Bernalillo Country, New Mexico came into the limelight in 2022.

According a 2022 IBM Report, the surveyed businesses lost $4.35 million per incident to data breaches. Ransomware has wreaked havoc on several enterprises and continues to be a potent business threat for many. Cybercrime might cost global enterprises $10.5 trillion annually by 2025. (Source: IDC)

The cybersecurity landscape is constantly getting remolded to accommodate the new remote work model.

Proactive Cybersecurity: Why It’s the First Step

As enterprises try to scale their businesses for added agility, resilience, and collaboration, they largely depend on technology to enhance their digital prowess. This kind of dependence calls for security countermeasures or security controls to safeguard the business, which, according to some studies, likely gets overlooked. 43% of cyber-attacks target small businesses, only 14% of these businesses can defend themselves while 69% of businesses think that cyberattacks are more targeted than before.

A small budget is allocated to security measures, while a bigger chunk goes to other operational activities. However, by simply following preventive measures like creating regular data backups, upgrading cybersecurity to combat risks, educating personnel on the importance of data security can help preserve the integrity of your business. Creating awareness about the risk can be the first step in the right direction.

In Focus: Machine Learning And Analytics in Cybersecurity

The traditional reactive approach needs to take a backseat so that a proactive PDR (Prevent – Detect – Respond) strategy can be used to deal with increasingly complex cyberattacks.

A data-backed strategy powered by analytics and technologies like Machine Learning (ML) are changing the cybersecurity paradigm.

ML-powered analytics security systems pack a punch when dealing with attacking methods and techniques. Environments with vast volumes of data can be handled effortlessly through such systems. 

Tracking and collating millions of external and internal data points across infrastructure and users is something enterprises need to do to ensure that their cybersecurity is robust. It is impossible to manage such tasks manually because it may impact efficiency due to increased cognitive load.

Using technologies like machine learning to enhance automation and analytics enables organizations to take a more proactive security stance. Because these technologies can proactively detect anomalies in numbers, patterns, and behaviors, isolating incidents and swiftly detecting those that require deep human analysis becomes easier.

ML and Cybersecurity: A Winning Combination

Machine learning and artificial intelligence (AI) aid organizations in enhancing their cybersecurity posture through data analysis and the detection of complex patterns. These technologies can be used to develop security models and algorithms that preemptively detect threats and predict how future attacks might look.

As a result, ML and AI are increasingly being used to power information security solutions such as SIEM, DLP, NGFW, NGAV, EDR, email filtering, and many more.

Behavior Prediction: Threat Profiling for Stronger Security Barriers

AI and ML-powered models crunch large amounts of data at breakneck speed and predict behaviors in ways that humans cannot. These models assist cybersecurity teams in creating threat profiles using data and predicting the next threat. These teams can then build security barriers and respond to threats proactively.

To detect anomalies in network behavior, ML algorithms constantly monitor network behavior. ML can analyze past cyberattack datasets to determine which networks were primarily involved in specific attacks. Its ability to process and analyze large amounts of data enables it to detect threats such as malware, policy violations, and internal threats.

Detecting Anomalies: Tightening the Security Perimeter

ML makes identifying bad neighbors easier. It also detects phishing traps and prevents users from connecting to unsafe websites through preemptively assessing internet activity, and detects threatful attacks, both old and new. This is significant because hybrid and remote working models create security vulnerabilities.

According to IBM, using compromised user credentials is the most common method to gain entry (over 20% attempts). Over 80% of employees admit to being careless about changing their passwords regularly. Because changing user behavior cannot be considered a security strategy, organizations must employ technologies such as machine learning (ML) to identify any virus or malware through changed behavior pattern rather than a signature.

Emphasizing on ML Proactivity

ML has become crucial for detecting malware that may be present on endpoints. Proactively identifying new malicious data and behavior with the help of known behavior models and malware characteristics ensures endpoints remain secure from malware attacks.

ML keeps the cloud safe because it can swiftly detect and analyze any abnormal activity in the cloud, such as suspicious cloud app logins, some geo-based anomalies and even conduct a quick IP reputation check proactively.

The technology makes detecting malware in encrypted traffic easier and faster. The algorithms help businesses identify encrypted traffic data elements.

Cybersecurity in the Future

With the shift to hybrid work, one aspect of improving cybersecurity is improving user behavior. To educate, train, and motivate employees to adopt safe practices is one way to go, but CISOs must also work to strengthen and enhance their security.

Using technologies like ML and AI to make cybersecurity efforts more competent and improve the enterprise's capacity to identify, isolate, and remedy breaches while ensuring a low impact will become critical.

Learn how Xoriant security solutions are aimed to protect your data-driven infrastructure from a wide array of threats.