Advancing Security | The Age of AI & Machine Learning in Cybersecurity

Modern cyber attackers’ tactics, techniques, and procedures (TTPs) have become both rapid and abundant while advanced threats such as ransomware, cryptojacking, phishing, and software supply chain attacks are on an explosive rise. The increasing dependence global workforces have on digital resources adds another facet to a growing cyber attack surface we all now share. In an effort to stand up to these challenges, businesses task their CISOs with developing, maintaining, and constantly updating their cybersecurity strategies and solutions.

From a tactical standpoint, CISOs ensure that their business’s security architecture can withstand the ever-shifting modern threat landscape. This means choosing the right tool stack that is capable of combating complex cyber threats at the breakneck speed in which they appear. As single-layer, reactive security solutions can no longer keep up with increasingly sophisticated cybercriminals, CISOs now have to stack multi-layered and proactive solutions together to build an adequate defense posture.

Advanced Threats Call for Advanced Solutions

Today, many CISOs know that artificial intelligence (AI) and machine learning (ML) are needed to accelerate and automate the quick decision-making process needed to identify and respond to advanced cyber threats. AI is designed to give computers the responsive capability of the human mind. The ML discipline falls under the umbrella of AI. It continuously analyzes data to find existing patterns of behavior to form decisions and conclusions and, ultimately, detect novel malware.

The task of building the right security stack is also one constantly under discussion, even on a federal level. In May 2022, the U.S. Senate Armed Forces Committee’s Subcommittee on Cyber held a congressional hearing on the importance of leveraging artificial intelligence and machine learning within the cyberspace. This hearing, including representatives from Google and the Center for Security and Emerging Technology at Georgetown University, discussed the use of AI and ML to defend against adversary attacks, effectively organize data, and process millions of attack vectors per second, far surpassing any human-only capability at threat detection.

The committee also highlighted a growing concern occurring in the cybersecurity space now: the “shortfall of technically trained cybersecurity personnel across the country in government and industry alike.” The global shortage is concerning, with over 2.7 million cybersecurity roles unfilled according to the 2021 Cybersecurity Workforce Study.

With a decrease in cyber expertise, the alert to response ratio can quickly overwhelm many in-house security teams. Leveraging AI can help overworked teams to scale up protective services and to automate and orchestrate complex, time-consuming response actions. All representatives underscored the value of harnessing AI in cybersecurity with its key benefits summarized below:

• Automated Attack Vector Processing – AI is able to process millions of vectors every second and combat emerging attacks by detecting new patterns in real-time.
• Zero-Trust Model Support – Human patterns are predictable and disparate data sets without AI are simply not useful nor actionable. AI helps build the complete threat analysis needed to sustain a working zero-trust model.
• Threat Operations Management – AI technology can augment cybersecurity teams by automating the interpretation of attack signals, prioritizing alerts and incidents, and adapting responses based on the scale and speed of the attacker.

Analog Players in a Digital World – The Shortcomings of Legacy AV

In a time long passed, the number of malware threats could be reasonably documented and accounted for. Back then, legacy anti-virus (AV) and anti-malware (AM) solutions offered businesses a means of blocking out known threats – malware variants that have already been discovered and assigned a signature which are then deployed to all protected endpoints. These legacy AV and AM solutions are signature-based, designed to flag known threats but blind to anything unexpected. This allows a gap to appear between the initial use of the malware and the existence of a new signature to block it.

The problem with today’s threat landscape is that threat actors have become incredibly skillful at creating novel malware. VirusTotal reports that it receives 2 million new samples every day. In 2021 alone, they reported that over a million samples signed with legitimate certificates were found to be suspicious. Only able to defend against known threats, legacy AV and AM are simply unable to keep up with the barrage of novel malware, ransomware, incoming zero-day vulnerabilities, or new hacker tradecraft.

During an attack, speed is crucial but legacy solutions like AV and AM are incapable of detecting and stopping malicious attacks in real time. AVs and AMs are only as good as their last update, and actionable analyses from previous attacks are usually weeks or months-old by the time they are usable by these solutions.

Why AI & ML Thrive in the Cybersecurity Arms Race

Artificial intelligence and machine learning can be leveraged very effectively against modern threats and their capabilities go far beyond the identification and flagging of known threats. They are designed to learn emerging threat patterns and identify new, malicious behaviors based on their similarity to existing exploitations, threat actor TTPs, and malware. The application of AI and ML is invaluable in bolstering an organization’s cybersecurity strategy.

1. Preventative Strategies & Response – With AI and ML, a security solution can autonomously detect and prevent malicious files and processes early in the attack lifecycle. Most commodity malware attacks can be prevented and remediated before they execute, reducing the attack surface and lowering the burden on the organization’s malware triage team.
2. Accelerated Threat Hunting – AI and Machine learning, coupled with strong monitoring capabilities, provide SOC analysts with deep visibility into what actually happened on a device during a cybersecurity incident. Rather than facing a long, manual triage process, analysts receive pre-correlated storylines that reveal the relationships between events, in many cases obviating the need to run further forensics tools.
3. Improved Security Policies – A security solution backed with AI offers users the ability to select the level of protection they want to automate. For example, in the case of a particularly critical device or user,  automatic remediation can be enabled on any suspicious activity. In other situations, a more permissive rule might be set, allowing suspicious activity to generate alerts but without any automated remediation.

The SentinelOne Approach | How AI & ML Augment Your Security

The best approach for CISOs building a scalable security stack is to converge AI and ML together with human expert analysts. A smart blend of these can amplify the strengths of a business’s IT team while covering any weaknesses and the key to this approach lies in automation. SentinelOne’s Endpoint Protection (EPP) and Endpoint Detection and Response (EDR) seamlessly combines automation with both AI and ML to detect and remediate modern attacks in real-time, at machine speed, and without extra intervention. This means that businesses can focus their resources on addressing operations-specific tasks. SentinelOne’s EPP solution also fully replaces legacy AV and AM solutions and can be scaled and tailored to fit a businesses’ specific requirements and processes.

SentinelOne focuses on acting faster and smarter through AI-powered prevention and autonomous detection and response. With the Singularity XDR Platform, organizations gain access to back-end data across the organization through a single solution, providing a cohesive view of their network and assets by adding a real time autonomous security layer across all enterprise assets.

Singularity Identity provides an easy to manage platform that prevents, detects, responds, and hunts in the context of all enterprise assets, allowing organizations to see what has never been seen before and control the unknown. It is the only platform powered by AI that provides advanced threat hunting and complete visibility across every device, virtual or physical, on prem or in the cloud.

Learn more about how Singularity helps organizations autonomously prevent, detect, and recover from threats in real time by contacting us or requesting a demo.