One can’t discuss the modern state of endpoint security without mentioning a term that has quickly become ubiquitous with security solutions: artificial intelligence (AI).
With a constantly evolving threat landscape and many security challenges plaguing organizations (e.g sprawling attack surfaces, monitoring and continuity gaps, alert overload, and limited resources), it’s clear that endpoint security must evolve as well, and the most-promising advancement is AI.
While some have utilized early iterations of this technology for decades, recent, rapid advancements in AI technology have caused it to enter the cybersecurity mainstream, with organization leaders and IT practitioners both seeking AI-powered technology to enhance workflows, enable better detections, and harden their attack surfaces.
Arctic Wolf’s Navigating the Human-AI Relationship for Security Operations Success, which recently surveyed global security leaders, found that 73% believe in the promise of AI in cybersecurity and are already implementing AI-driven solutions, with 79% of respondents indicating they were taking this approach because they believe AI will improve their ability to detect new threats.
While AI has many use cases and has transformed both security and the behavior of threat actors, the technology has also become a must-have in endpoint security, where its unique capabilities have shown promise in preventing, detecting, and stopping endpoint threats.
Key Components of AI in Endpoint Security
AI promises to further advance behavior-based endpoint threat detection. It thrives in understanding behaviors and turning them into patterns for precise identification and detection, which allows AI to greatly enhance endpoint security capabilities.
AI-enabled or AI-enhanced capabilities in endpoint security can include:
1. Contextual categorization: In most organizations today, each endpoint is treated the same, meaning each is secured with the same controls as every other endpoint. AI has the potential to gather contextual information about the endpoint — device characteristics, location, user role, sensitivity of applications and data it invokes — and recommend and/or adjust security controls accordingly.
2. Malware detection: Detection of various types of non-traditional malware — fileless malware, polymorphic malware, and zero-day malicious code — can be aided by use of AI.
3. Behavioral analytics: By establishing a baseline of “normal” activities on the endpoint, AI can facilitate faster, more accurate detection of previously unknown malicious activity, as well as better determine whether anomalous activity — unusual but not showing clear signs of malice — is malicious by quickly analyzing large datasets and identifying patterns.
4. Alert investigation and analysis: When a potential threat is identified and triggers an alert, AI can facilitate the automated enrichment of the alert case file, adding related contextual information, such as research on hash values, initiating dynamic file analysis, etc., in turn accelerating the investigation and reducing the time needed for human analysts to reach a decision on follow-on actions.
5. Incident response: While most response automation actions today are based on rules and playbooks, AI can take orchestration and automation to the next level — identifying and, as permitted, executing next best actions to manage an incident on a case-by-case basis, quickening and simplifying the path to remediation-response automation for many organizations.
Other notable capabilities include hard-to-detect malware-specific detections, threat intelligence correlation, network traffic analysis, predictive analysis, and continuous behavior and detection learning.
These capabilities are vital to the future of endpoint security not only because (as we’ll discuss below) they increase the efficiency and efficacy of both security solutions and security teams, but also because the threat actors are always seeking new approaches to outsmart traditional endpoint security.
AI vs. Signature-Based Detection in Endpoint Security
Signature-based detection is the more traditional approach to endpoint security threat detection: the system seeks out and alerts entirely based on known threats, meaning malicious code or actions that have been previously entered into the technology’s rule set. AI, however, can facilitate detections based on behavior, has predictive threat detection capabilities based on previous detections, can contextualize and analyze actions, and continuously learns based on both historical and new threat actor data.
Why is that distinction important? Because, in 2024 alone, over 500,000 new malware samples were identified daily.
Given the volume of new malware occurring, not to mention the proliferation of polymorphic and fileless malware, rules-based tooling can quickly get stuck in a cycle of reacting to threats, always a step behind malware evolutions and beholden to the fine-tuning of possibly under-resourced security teams. For example, if a trusted process (like PowerShell) suddenly starts encrypting files or pulling commands from the web, AI models can flag this — even though there may be no file or known signature to detect. A rules-based system may miss this unusual event, as static analysis isn’t able to detect underlying intent (i.e. why the files are being encrypted) or semantic behavior (i.e. that this new behavior from PowerShell may be suspicious).
Without help from AI , staying ahead of threats may soon become impossible. Given that, according to The State of Cybersecurity: 2025 Trends Report, 68% of organizations have experienced one or more endpoint attacks that successfully compromised data and/or their IT infrastructure, it’s clear that endpoint risk is highly prevalent, and endpoint security must evolve to meet this new wave of threats.
Additionally, malware deployment is just one tool in an attacker’s toolbox. Ransomware, a top threat globally, often targets the endpoint early in the attack chain. There are many actions a ransomware gang or individual threat actor can take (e.g. credential-based attacks, lateral movement commands, permission changes within an endpoint), so having a solution in place that can quickly and adaptively identify and analyze suspicious behavior on an endpoint, versus a set of static capabilities, can help an organization detect and respond to ransomware faster and more effectively.
AI can be a force multiplier. It promises to further advance behavior-based detection, and it thrives in understanding behaviors and turning them into patterns for precise identification and detection, which allows AI to greatly enhance security capabilities.
Benefits of Utilizing AI in Endpoint Security
AI can, at scale, decipher usual from unusual user behavior and access events with precision. AI can also, simply, conduct analysis and reach conclusions faster than humans, which in turn offers many potential benefits: reducing alert noise, improving filtration of alerts, shrinking investigation and response times, and providing precise, accurate information to security analysts.
Organizations are starting to understand these benefits. According to Navigating the Human-AI Relationship for Security Operations Success, 39% of cybersecurity purchases or renewals are currently dependent on the presence of AI within the vendor’s offering, and 72% of buyers are looking to AI for better threat prediction and prevention, with 70% expecting to increase their detection capability through AI models and technology. Additionally, AI is considered better at maintaining accuracy at speed when identifying threats (69%) and minimizing errors (66%), according to those surveyed. It’s clear AI is rapidly influencing how organizations view the future of their endpoint security.
In short, predictive AI can help your organization move into a proactive endpoint security stance, moving from continually “detecting and responding” to threats to “preventing future threats.”
Potential benefits of utilizing AI within your endpoint security solution include:
- More precise threat detections
- Detections that occur earlier in the potential attack chain
- More enriched alerting and alert analysis
- Automated response actions to suspicious activities or threats
- Predictive threat analysis, which hardens the attack surface against new threats
- A reduction in false positives
- A reduction in alert noise, which alleviates alert fatigue within security teams
- The ability to integrate threat intelligence, allowing for better protection against, and detection of, threats
- The closing of organization-wide operational and security gaps
- Increased efficiency and effectiveness of IT and security teams
- The ability to scale both endpoint and overall security operations more effectively
While these benefits can, and have already been shown to, transform endpoint security, it should be noted that AI is not the single answer to all cybersecurity problems.
The Future of AI in Endpoint Security
AI is set to transform endpoint security in the years ahead. Among the most impactful developments will be the rise of AI-powered technologies that use increasingly advanced algorithms to predict and stop threats before they can take hold.
Emerging capabilities include next-generation detection systems that apply deep learning and neural networks to process massive volumes of data, delivering faster, more precise identification of malware and other threats.
While AI won’t fully replace human cybersecurity professionals, it serves as a powerful force multiplier. Its strength lies in rapidly analyzing massive datasets and spotting patterns, enabling it to take on repetitive tasks and surface valuable insights. However, human expertise remains critical for higher-level strategy, interpreting complex situations, and tackling advanced threats that demand judgment and context.
The Value of Partnering with a Trusted AI-Powered Endpoint Security Provider
Arctic Wolf® understands that technology alone can’t End Cyber Risk®, but that the right technology, properly managed can help advance security outcomes. Arctic Wolf Alpha AI enables our experts and customers to achieve the security outcomes they are striving for by accelerating our teams’ and solutions’ ability to reduce noise, increase fidelity, provide better context, and offer security guidance in the face of ever-evolving cyber threats.
Arctic Wolf Aurora™ Endpoint Security is AI-driven, delivering advanced prevention, detection, and response, stopping threats before they disrupt your business. With the support of the Aurora Platform, Alpha AI, and the Arctic Wolf Concierge Experience , your security teams aren’t left to configure and maintain endpoint security alone. Our technology is backed by both an industry-leading open XDR platform that can collect, enrich, and analyze data at scale, and world-renowned security experts who provide triaged alerts, continuous tailored guidance, and on-demand expertise for all your endpoint security needs.
Take a deep dive into how AI is accelerating endpoint security outcomes by viewing our webinar, Complete Endpoint Security: AI Powered, Expert Managed.
Explore the future of endpoint security solutions further with the 2025 Gartner® Voice of the Customer for Endpoint Protection Platforms.
