Generative artificial intelligence (AI) has garnered considerable attention in recent times due to its vast potential in various applications. However, AI has long been employed in the field of cybersecurity. In particular, endpoint security, a crucial component of cybersecurity relies heavily on AI.
In this analysis, we will delve into the realm of endpoint detection and response (EDR), a subset of endpoint security, and examine the pivotal role played by AI in this domain. We will begin by defining EDR and then explore the numerous benefits that arise from integrating AI into endpoint security. Furthermore, we will speculate on the potential advancements on the horizon and their impact on endpoint security systems.
What Is Endpoint Detection and Response (EDR)?
EDR is a cybersecurity technology designed to monitor and protect endpoints. Endpoints are physical devices such as mobile phones, laptops, Internet-of-Things (IoT) devices, corporate workstations, or point-of-sale terminals. Unlike web endpoints, which refer to specific URLs or web addresses, EDR focuses on the security of physical devices.
EDR plays a vital role in detecting and responding to potential threats by providing fine-grained security incident detection and investigation capabilities. It helps identify and remediate security incidents effectively, ensuring the overall safety of an organization’s endpoints. In the current work-from-home conditions, EDR’s complexity has increased. With a shift away from on-premise computing and traditional corporate networks towards hybrid, managed, or cloud-based services, the risks associated with hacks and malware insertion have become more prominent, and tougher to track.
With the rising adoption of both cloud-based and on-premises EDR solutions, the global market for EDR is projected to experience substantial growth. According to the Endpoint Detection and Response – Global Market Outlook report, this market is expected to grow at a rate of 26% annually, reaching a valuation of $7.27 billion by 2026.
How EDR Platforms Can Leverage AI and ML
AI has already been adopted by many cybersecurity platforms, enabling effective threat detection and protection. EDR, in particular, serves as an ideal data collection point, allowing AI algorithms to determine if actions deviate from the norm.
Data analysis plays a crucial role in EDR, helping to establish a baseline for normal behavior and enhancing behavioral analysis to identify anomalies. “EDR systems are continuously collecting and analyzing data on endpoints,” says George Symons, Persistent Systems Vice President, Strategy for Cloud, Infrastructure, and Security. “This is an ideal use case for AI/ML.”
AI can also assist in mitigating human errors, as people are often the weakest link when protecting against cyberattacks: “Aggregating the information across many systems further increases the accuracy of AI components for greater accuracy in determining anomalous events and eliminates false positives, thus reducing alert fatigue of the IT administrator or cyber analyst,” says Symons.
Benefits of Using AI in EDR
One advantage of using AI in EDR is improved threat detection. And as AI and machine learning algorithms ingest more datasets and become smarter, they will likely improve their threat detection capabilities. A report by Blackberry Cylance titled AI-driven EDR found that 70% of respondents reported using AI in their threat prevention strategies, emphasizing the prevalence of AI adoption in this domain. This can lead to the detection of more breaches and decrease the time it takes to identify them. It also helps reduce false positives, providing more accurate alerts.
In addition to improved threat detection, AI in EDR accelerates threat response. By automating specific remediation processes, new technologies can expedite response times and free up scarce engineering talent. “More and more detected breaches will be able to be self-contained and remediated without human touch, expediting response times and enabling security analysts to dedicate more of their resources on breaches that require security analysts to respond to,” explains Tomy Han, Partner at Volition Capital.
Furthermore, leveraging automation is crucial for maintaining a strong security foothold. AI in EDR can help organizations take proactive measures to make endpoint cybersecurity more effective. “With the growing attention towards generative AI, we believe there will be many vendors that help cybersecurity be more proactive in an automated fashion over time,” explains Han, “whether it’d be auto-generating vulnerability patches as vulnerabilities become detected or auto-creation of real-world attack simulations for enhanced purple teaming.”
Moreover, AI-driven automations go beyond human capacity, as they can identify threats that humans might overlook. The aforementioned report found that 78% of respondents acknowledged that AI technology had discovered threats that humans couldn’t see. AI can be leveraged to automatically classify events and processes based on predefined deny or allow lists while continuously monitoring for deviations.
Limitations of AI Within EDR
According to Han, “Bad actors are constantly looking for holes in an organization’s security posture and will likely be incorporating AI themselves to breach an organization and remain undetected.” This highlights the potential for a battle of AI versus AI in the cybersecurity landscape. The side that possesses a larger amount of data and implements the right procedures to train AI models will have an advantage in this ongoing struggle.
Therefore, despite the emergence of AI in the cybersecurity realm, organizations must remain vigilant to prevent phishing campaigns, psychological attacks, and other malicious activities. It is still imperative to enforce security measures such as multi-factor authentication (MFA), authorization protocols, encryption, and more. These safeguards help fortify the organization’s overall security posture.
While AI can play a significant role in offensive and defensive cybersecurity efforts, it is important to remember that it is not a foolproof solution. Human vigilance, adherence to cybersecurity frameworks, and the enforcement of robust security measures are essential components in maintaining a solid defense against evolving threats.
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With rising cyberattacks and the decentralization of corporate IT, EDR is a common need across organizations. That said, EDR is an umbrella concept, and it will require the combination of multiple tools and best practices to fully safeguard all endpoint devices. To further this agenda, AI certainly has a role to play within endpoint security.
By looping in AI within EDR, organizations can start to mitigate many potential threats by first identifying patterns in endpoint-related events and using this baseline to detect possible incidents. The use of AI can help protect endpoints across the threat landscape by decreasing response times and helping to prioritize alerts.
Yet, as previously mentioned, the future of AI could very well become an arms race between software owners and hackers. In this world, the victor will be determined by the quality (and amount) of data the AI is trained upon, the smartness of the algorithm, and its continual improvement.
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