The Role of AI and ML in Shaping the Future of Endpoint Security

The digital era opened up a world of opportunities. But at the same time, it increased the threat surface. Today, the proliferation of remote and hybrid work and the increasing use of personal devices have expanded the threat surface, making it challenging to distinguish noise from actual signals that point out cyber threats.

As the security perimeter dilutes with digitization and digital transformation, the focus on endpoint security continues to increase.

In fact, the global endpoint security market is projected to grow to $24.58 billion by 2028 from $13.99 billion in 2021 at a healthy CAGR of 8.3%.

While most CISOs have re-evaluated their security policies to enable work from home and help virtual teams to respond to the pandemic, the endpoints often lack needed patches to stay secure.

A 2021 report on endpoint risks identifies that remote devices, inconsistent patching, and failing security controls continue to challenge enterprise security. Endpoint complexity is further increasing as more business-critical applications get added to the IT mix to support new ways of working.

As vulnerabilities continue to pose significant risks, technologies like machine learning (ML) and artificial intelligence (AI) can help enterprises improve their endpoint security stand and change how cybersecurity is managed.

Driving Zero-Trust Security

Zero trust is a cybersecurity paradigm that takes a proactive, integrated approach to security across all digital layers. It continuously verifies every transaction, asserts the least privilege, and employs intelligence, and advanced detection, to drive real-time threat responses. Zero trust adopts a “never trust, always verify” approach.

Technologies such as ML and AI help organizations adopt a risk-based security stand and enable zero-trust security. AI and ML evaluate user requests in real-time, assess the security context such as the device, network, and related behavioral data, and generate a risk score. This risk score can then be employed to allow or deny access and fine-tune access management policies to drive stringent authentication.

Enforcing Standardized Security Policies at Scale

With thousands of devices and numerous access points, enterprises can no longer control the networks or devices users use to access data. Enforcing standardized policies at scale becomes crucial to drive endpoint security and reliably detect violations.

AI and ML rescue the enterprise by helping them develop the mechanisms that allow them to enforce standardized security policies at scale. These technologies can assist enterprises in automatically adjusting access policies according to real-time analysis of behavioral patterns.

This makes endpoint security more robust and proactive since it reduces the dependence on IT and security teams to continuously review access requests and manually grant access.

More profoundly, AI and ML can capably work to implement rule-based policies automatically to make access management more straightforward and secure. The end-user also does not need to wait for approvals — the fast access drives the user experience.

Proactive Security Stand

AI and ML technologies improve the legacy antivirus technologies and help enterprises leverage Next-Generation Anti-Virus (NGAV). NGAV allows enterprises to protect endpoints against zero-day attacks, fileless attacks, and evasive malware. These attacks are hard to detect using the traditional antivirus since they change their source code to avoid detection.

NGAV employs AI and ML to detect unknown threats using string analysis, N-gram analysis, Control flow graph (CFG), etc.

AI and ML also drive Extended Detection and Response (XDR) by correlating and analyzing data from various sources to identify evasive threats.

XDR is powered by machine learning to identify highly sophisticated threats, track threats across multiple system components, and investigate threats faster and more securely.

Improving Endpoint Visibility

As cyberattacks, ransomware, hackers, and bad actors increase in sophistication, relying on AI and ML to improve endpoint security is gradually becoming non-negotiable.

ML and NLP technologies improve endpoint visibility by discovering and mapping endpoints across an organization and providing complete clarity on the external attack surface protection readiness.

AI-based real-time authentication and behavioral analytics drive better security against lost devices by improving endpoint management. These technologies allow enterprises to adapt security policies and roles to each user in real-time according to device type, configuration, and several other variables.

They also identify the patterns of where and when the users attempt to log in. With granular visibility, enterprises can protect themselves against device and app cloning and user impersonation.

Improving Patch Management, Reduce Ransomware

Most of the lethal ransomware attacks last year — Colonial Pipeline, Kaseya, etc. — occurred because the endpoints were not updated on patches.

Going forward, endpoint security has to improve patch management and ensure that it happens intuitively. Patch management must move out of the inventory and fleet-based approach and become more adaptive and intelligent.

AI and ML technologies will have to power IT asset management and help enterprises gain real-time visibility and control of every endpoint. Employing these technologies will help enterprises keep up with the growing complexity of threats and ensure that patch management is never approached with incomplete data.

In essence, AI and ML allow data-driven patch management and help enterprises prioritize and quantify adversarial risks based on factors such as in-the-wild exploit trends, security analyst validation, threat intelligence, etc. This approach improves patch reliability while providing complete visibility into the endpoint security posture.


AI and ML will (and should) become intrinsic to endpoint security solutions sooner than later. This is primarily because the need to quantify risks wherever possible in endpoint security is only increasing, and the push for endpoints to become more self-healing is accelerating.

That said, AI and ML are poised to transform endpoint security. These technologies are gearing up to enable enterprises to identify anomalies and threat hunt in real-time and unravel complex ransomware threats before they can cause real damage. Enterprises can then enforce the least privileged access while treating each identity as a new security perimeter.

As the world of work adopts new ways of working, the focus on endpoint security will only increase. AL and ML will provide critical insight about incidents, empower an organization to respond faster, and make the security perimeter more relevant to the new world.

Connect with us to learn more about how AI- and ML-powered endpoint security engineering can help you transform endpoint security and prevent cyberattacks.