10 Ways AI Agents Are Transforming MDR & XDR Services

Manage Detection & Response (MDR) and Extended Detection & Response services have come a long way since MDR was openly defined in 2016 (although many would consider the activity had been around for a lot longer!). The emergence of the Managed Security Service Provider (MSSP) in the last 10 years or so has focused attention on customer requirements for a much more specialised role. The rapid development of AI tools in the last three to four years has seen specific technologies become available that MDR providers can potentially benefit from, helping to turn reactive tools into potentially autonomous systems capable of operating at a scale and speed that humans alone cannot match.

The following is not an exhaustive list, however we have chosen ten areas where we believe that AI and AI agents are providing some kind of potential benefit to MDR & XDR providers. These benefits are worth considering if you are considering contracting for services with an MDR or XDR provider.

Note that while we’ve provided some examples of vendors and how they use these tools, this is not intended to be an exhaustive list of companies. Many of the big names in MDR/XDR - Microsoft, CrowdStrike, Palo Alto Networks et al - will have multiple offerings that use many - if not all - of these technologies, and it would be too much to list them in every area. We’ve therefore tried to include a variety of different vendors for each example to illustrate how extensive these tools are being used across many different sectors of the market, and the breadth of functionality on offer.

10 Ways To Transform MDR & XDR

#1. Behavioral Analytics (Anomaly Detection)

A key benefit of AI agents is that they can quickly analyze historical data and user behavior to identify patterns that deviate from the established "norm,". This often indicates hidden or novel threats that could otherwise be overlooked.

Examples:

• AhnLab's AI Plus security platform provides comprehensive protection for endpoints. The firm says it offers advanced threat prevention by using AI agents to interpret the causes, flows, and contexts of attacks.

• The ESET PROTECT Platform leverages AI-native protection to detect anomalies missed by traditional rules. Its AI Advisor agent acts as a generative AI-based assistant integrated with ESET Inspect (the XDR-enabling module of the ESET PROTECT Platform).

#2. Alert Triage and Summarization

When harnessed well, AI agents can automatically evaluate large volumes of incoming security alerts to determine their severity, summarizing complex logs into actionable insights and prioritizing critical risks for analysts.

Examples:

• Dropzone AI uses several different role-based agents for specific tasks. These include one that acts as an autonomous SOC analyst that it says can investigate alerts 24/7.

• Intezer Labs' AI SOC agent provides forensics-based alerts, emphasizing speed of triage and reduced escalations to human analysts.

#3. Cross-Domain Signal Correlation

While XDR provides the integration layer, MDR operates the "human-led" services on top. When required, AI agents have the potential to more quickly connect disparate telemetry data across identities, endpoints, networks, and cloud workloads to identify a single, complex attack chain.

Examples:

• CrowdStrike's Falcon Platform provides unified, cross-domain protection across all enterprise risk areas, and similarly, SentinelOne's Singularity Platform uses an AI-powered unified data lake to correlate telemetry autonomously.

#4. Rapid-Response Containment

Implemented correctly, AI agents can enable advanced, automated workflows that take immediate action the moment a threat is verified. This could include some of the most timely and critical responses, such as quarantining a compromised device, blocking malicious code execution, or revoking access.

Examples:

• ReliaQuest’s GreyMatter security operations platform is designed to detect and contain threats quickly, including in multi-MDR environments, using its role-based autonomous Agentic Teammates

• Microsoft Defender provides a wealth of agentic AI features, including autonomous defense to block high-speed attacks before they spread via its Security Copilot offering.

#5. Autonomous Remediation

Depending on the level of autonomy, AI agents can find and "fix" vulnerabilities by automatically applying software patches and updates across a global network without manual IT intervention. A note of caution should be given here, as many vendors, service providers and CISOs are wary of giving agents full autonomy without guardrails in place - or a "kill switch" to shut an errant agent down....

Examples:

• For patch management, Action1 provides an autonomous remediation engine that bridges the gap between vulnerability discovery and automated patching, with built-in agent audit trails.

• For MDR providers looking at managed vulnerability management, Mondoo offers an agentic service that identifies and fixes policy violations automatically at machine speed.

#6. Policy and Access Optimization

Fast and efficient identity and access controls are key for modern security operations - including for agentic AI. Agents that continuously monitor the security environment and dynamically adjust access rules, security policies, or guardrails in real-time can be a much more efficient way to close exploitable gaps.

Examples:

• Cyata provides a posture-first platform that uses adaptive guardrails to control agentic identities, protecting companies from the risks of autonomous agentic workflows.

• Skyrelis offers behavioral monitoring and runtime policy enforcement for AI agents, tools, and data via a policy layer that adapts security controls across users and geographies in real-time.

#7. Reducing MTTU and MTTR

AI can potentially reduce the Mean Time to Understand (MTTU) ie. how long it takes to figure out what happened, and the Mean Time to Respond (MTTR) - basically how long it takes to fix it - by translating complex data into plain-language insights.

Examples:

• Protos Labs builds AI agents that it says reason like human analysts to significantly accelerate investigations in threat intelligence operations.

• Prophet Security’s AI SOC uses agents to bring context and reasoning to flagged alerts, thereby helping human teams act faster.

#8. Forensic Augmentation

As the complexity of threats grows, the use of AI assistants and agents can provide analysts with deeper context and automated evidence collection at scale, significantly accelerating the forensic analysis of security incidents. When combined with industry or sector-specific technologies, the agent's capabilities can be enhanced to tackle niche use-cases.

Examples:

• SentinelOne's Purple AI accelerates investigations by providing comprehensive details about incidents, as well as answering natural language queries about threat data.

• Test and measurement specialist, VIAVI Solutions, offers its XEdge Sensors that bring packet-level forensics to speed up detection and resolution for edge-based, network infrastructure.

#9. Agentic SOC (Human-Led, AI-Operated)

One of the most-discussed and fiercely debated AI/agentic AI topics, the Agentic SOC is a next-generation security operations model where a fleet of autonomous AI agents handles the bulk of triage, investigation, and remediation, while human experts focus on high-level strategy and policy orchestration.

Examples: Many firms already offer agentic SOC capabilities - and more will follow in 2026 - but the following vendors illustrate the kinds functionality that AI agents can offer.

• Anomali and Elastic both offer Agentic SOC platforms where SIEM and XDR functions are driven by AI automation

• Elsewhere, Microsoft is developing its Security portfolio along the lines of an agentic platform as it doubles-down on tools built specifically for this "AI-operated" era.

#10. Security Data Fabric

While data fabrics are arguably more of an architectural strategy than a set of features or functions, they do provide a layer that unifies enterprise data from disparate sources in a clean, structured, way. In theory, this provides an AI-ready foundation that agents can use to perform investigations at machine speed.

Examples:

• Cribl creates an AI-ready security data foundation by unifying, enriching, and routing telemetry.

• Cisco's Data Fabric unifies machine data to enable real-time, AI-powered threat detection and response.

The Gist

As attackers use AI to create threats at scale and at machine speed, agents are already being used by many MDR and DXR vendors and service providers to supply autonomous features that assist human operators and augment existing security tools. From detection through triage and ongoing threat intelligence, agents can be a valuable tool in the security professional's arsenal, but with autonomous capability comes increasing risk. When contracting with a supplier of security services that uses AI agents, companies should check for governance and guardrails, including audit trails and human oversight. AI agents will become increasingly pervasive, and used wisely, can help combat the efforts of bad actors looking to exploit vulnerabilities.