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What MDR does—and what AI changes
MDR is a managed service, not just a detection product. A provider monitors security signals, investigates suspicious activity, helps determine whether an incident is real, and either responds or guides the customer’s team. Microsoft describes its analysts as managing the incident queue around the clock, triaging and investigating incidents, then taking action or guiding the customer through response. By contrast, endpoint detection and response (EDR) and extended detection and response (XDR) products generate detections and incident data; a team still needs to monitor and act on those signals. Microsoft’s MDR service overview explains that service boundary.
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Traditional MDR should not be understood as analysts manually inspecting every alert. Mature human-led services already use automated resolution for known-good behavior, playbooks, detection engineering, reporting, service-level agreements (SLAs), threat hunting, and response actions governed by rules of engagement. Those capabilities appear as baseline MDR features in CRITICALSTART’s December 2024 buyer’s guide. A fair comparison is therefore between different levels and forms of automation, not automation versus no automation.
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“AI-driven MDR” has no universal definition established by the sources cited here. Providers may use AI or other automation at different points in the workflow, and a marketing label does not establish what happens operationally. Ask the provider to identify separately what is model-generated, what follows deterministic rules or playbooks, what requires analyst review, and what the customer can control.
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Where AI can change the security team’s workflow
An MDR workflow can be thought of as a chain: collect telemetry, identify potentially suspicious activity, enrich and correlate it, triage and investigate, then recommend or execute a response. AI-assisted systems may speed up or scale particular links in that chain. A provider may use AI for detection engineering, enrichment, correlation, triage, investigation support, response recommendations, or execution. These are distinct functions; a capability in one phase does not prove automation across the rest.
| Workflow stage | What automation may do | What the security team should verify |
|---|---|---|
| Detection and enrichment | Analyze signals, add context, or connect related events before an analyst reviews them. | Which telemetry sources are covered, which integrations are supported, and whether context is produced by a model, deterministic logic, or both. |
| Prioritization and triage | Sort alerts, identify likely benign activity, or recommend a priority for investigation. | How the provider defines a false positive and triage accuracy; what confidence thresholds apply; how analysts correct or override a result. |
| Investigation | Help assemble evidence or summarize activity for an analyst. | Whether analysts validate conclusions, what evidence is retained, and how complex or ambiguous cases are escalated. |
| Response recommendation | Suggest a containment or remediation action based on the investigation. | Whether a human must approve the recommendation and who has contractual authority to approve or decline it. |
| Response execution | Carry out a bounded action, such as an action permitted by configured rules or customer-defined guardrails. | Exactly which actions are allowed, when they run, whether they can be reversed, how exceptions are handled, and what is recorded in the audit trail. |
“Agentic” or “AI-native” language does not answer those questions. Ask for the actual workflow and control points, not just a description of the model or a claim that the service uses AI.
What changes—and what does not—for analysts
When routine enrichment, alert sorting, or well-defined response steps are automated, analysts may spend less time on repetitive handling and more time on complex investigations, threat hunting, contextual analysis, and response coordination. That is a change in the allocation of work, not evidence that human expertise is no longer needed. IDC’s 2025 assessment for Asia/Pacific excluding Japan says that, although GenAI and agentic AI are increasingly embedded in MDR workflows, human-led threat hunting, contextual analysis, and incident-response expertise remain critical. The regional scope matters: this is buyer guidance for Asia/Pacific excluding Japan, not a universal service guarantee. IDC MarketScape: Asia/Pacific (Excluding Japan) MDR Services 2025.
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Providers also describe different human-accountability arrangements. Sophos says its analysts supervise AI and retain accountability for decisions. CrowdStrike describes customer-defined bounded autonomy and guardrails, including customer control over when automated actions occur. Those are descriptions of the named providers’ approaches, not a shared industry standard; a prospective customer should confirm the specific contractual terms and operating controls that apply to its service.
The security team’s responsibilities still include setting policy, deciding which business risks are acceptable, providing context the provider may lack, approving or constraining disruptive actions, and owning internal communications and recovery. A provider may investigate and contain an event, but an organization still needs to establish who can authorize actions such as isolating a critical device, disabling an account, or blocking activity that could interrupt operations.
What survey and vendor figures can—and cannot—tell you
The 2025 SANS Detection and Response Survey illustrates why automation is attracting attention while operational challenges remain. SANS reports that 73% of respondents cited false positives as a top detection challenge, 90% relied on automated tools for detection, and 76% planned to expand AI and machine-learning use in detection and response. It also reports that 59% cited a lack of skilled personnel as a top detection challenge and 56% cited skill gaps as a leading barrier to response. These are survey findings, not proof that a particular MDR service will resolve those problems. SANS 2025 Detection and Response Report: Key Findings.
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The same report says 66% of respondents had at least partial automated response, while 13% reported full automation. Those figures describe respondents’ reported adoption, not how effective or safe their automation was. They also show why “automated” needs qualification: partial and full automation are meaningfully different operating models.
Provider performance figures should be read as claims about the provider’s own service and metric definitions, not as a head-to-head test. Sophos reports that 52% of cases are resolved end-to-end without human intervention and an average of 89 seconds from alert to automated response on its current MDR service page, accessed in 2026. CrowdStrike announced Charlotte AI Detection Triage general availability in February 2025 and reported over 98% triage accuracy, defining accuracy as agreement with expert decisions by its Falcon Complete MDR team. It also reported more than 40 hours of average weekly manual work eliminated, based on an assumed five-minute triage time per alert, with results varying by alert volume. The measures are different and neither figure establishes that one provider is faster or more accurate than another.
| Publisher and figure | What the figure describes | How to interpret it |
|---|---|---|
| Sophos: 52% of cases; 89 seconds average | Sophos says 52% of cases are resolved end-to-end without human intervention and reports an average of 89 seconds from alert to automated response. | Vendor-reported figures on its service page accessed in 2026; not an independent or cross-provider comparison. Sophos MDR. |
| CrowdStrike: over 98% accuracy | CrowdStrike defines the figure as agreement with expert decisions by its Falcon Complete MDR team. | Vendor-reported metric for its AI triage capability, announced as generally available in February 2025; not directly comparable to Sophos’s case-resolution or response-time figures. CrowdStrike announcement. |
| CrowdStrike: more than 40 hours weekly | CrowdStrike’s estimate of average manual work eliminated, calculated using an assumed five-minute triage time per alert. | The company says results vary with alert volume. Ask how the estimate maps to your own alert load and workflow; do not treat it as guaranteed staffing savings. CrowdStrike announcement. |
Differences in definitions, alert volume, telemetry, response authority, and measurement periods can make similar-looking numbers incomparable. For a procurement decision, request the calculation method, denominator, period measured, exclusions, and a service-level commitment that applies to your environment.
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Service examples: scope matters more than the label
Named services and capabilities illustrate why “AI MDR versus traditional MDR” is not a clean product category split. They cover different telemetry and responsibilities, and some examples are MDR services while others are AI triage capabilities rather than full MDR offerings.
| Example | Documented scope or control | What it means for evaluation |
|---|---|---|
| Microsoft Defender Experts MDR Plan 1 | Covers Microsoft Defender workloads and includes analyst triage, investigation, threat hunting, and response action or guidance. | Confirm that your required telemetry is within the supported Microsoft workload scope and clarify which response actions the service can take. |
| Microsoft Defender Experts MDR Plan 2 | Adds selected third-party signals collected through Microsoft Sentinel; Sentinel is a prerequisite. Microsoft documentation states a 1,500-seat minimum and advises confirming availability with Microsoft. For supported third-party products, the service provides response guidance. | Do not assume it directly executes response actions in every third-party product. Seat minimums and availability are dynamic terms; verify current eligibility and commercial details with Microsoft. Microsoft service documentation. |
| Sophos MDR | Sophos describes an AI-native system with analysts supervising decisions, governance, and accountability, and reports more than 500 third-party integrations. | The integration count and automation description are vendor claims. Check whether your specific products and telemetry are supported and what each integration permits the provider to do. Sophos MDR service page. |
| CrowdStrike Charlotte AI Detection Triage | CrowdStrike announced general availability in February 2025, describing customer-defined bounded autonomy and guardrails for triage and response workflows. | This is an AI triage capability; it is not automatically a like-for-like full MDR service comparison. Verify the service wrapper, analyst access, telemetry coverage, and response terms separately. CrowdStrike announcement. |
These examples do not establish that one service is better. They show that workload coverage, third-party signal handling, automation boundaries, and analyst responsibilities need to be compared separately. Microsoft’s stated Plan 2 seat minimum and availability guidance may change, so confirm the current terms before relying on them.
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How to compare MDR providers
Use the same scenario and questions with every provider. Ask them to walk through the handling of a realistic alert from your environment, from initial signal to closure. The goal is to uncover who does what, which systems are covered, and what happens when automation is uncertain or an action is risky.
- Map the telemetry. List endpoints, identities, cloud services, email, network systems, and other sources that matter to your organization. Ask which are supported, which data is collected, whether coverage depends on a specific platform, and whether integrations are read-only or permit response actions.
- Trace one alert end to end. Ask where AI, deterministic rules, playbooks, and analysts each enter the workflow: detection engineering, enrichment, correlation, triage, investigation, recommendation, and execution. Request a representative case and a description of the evidence available to your team.
- Define response authority before an incident. For actions such as isolating a device, disabling an account, or blocking activity, establish whether the provider may act independently, must request approval, or can only advise. Specify customer-defined limits, exceptions, approval routes, and how actions are reversed if needed.
- Test escalation and analyst access. Confirm 24/7 human availability, how to reach an analyst, what severity triggers escalation, how urgent cases are handed off, and who remains available if automation fails or confidence is low. A promised automated workflow is not a substitute for a usable escalation path.
- Compare investigation and response depth. Ask whether threat hunting is proactive or only triggered by alerts, what incident-response and forensic work is included, which actions fall outside the service, and what additional customer resources may be required.
- Make outcomes measurable. Review SLAs and ask how the provider measures acknowledgement, investigation, containment, false positives, and closure. Request definitions and denominators, not just target numbers. An outcome measure should state what event starts the clock, what pauses it, and what remedy applies when the commitment is missed.
- Inspect evidence and accountability. Verify that decisions, model-generated recommendations, analyst changes, approvals, and executed actions are logged and available to your organization. Ask who is responsible for decisions under the contract and how the provider supports an audit or post-incident review.
- Check regional and operational fit. Validate language, time-zone coverage, geographic handling of data, data-residency terms, regulatory requirements, and integration with your existing hybrid or multivendor environment. IDC’s 2025 Asia/Pacific excluding Japan guidance emphasizes outcomes, the balance of automation and human expertise, regional alignment, and hybrid/multivendor integration.
CRITICALSTART’s buyer’s guide also treats analyst access, rules of engagement, SLAs, and reporting as buyer criteria, while IDC emphasizes measurable outcomes and regional fit. These are useful evaluation dimensions regardless of whether a provider markets its service as AI-driven.
Questions to put in the contract and proof-of-concept plan
- Automation inventory: Which steps are automated today, which use AI, and which are planned rather than available? What is deterministic, and what depends on a model output?
- Approval and guardrails: Which actions can occur without approval? Can your team configure thresholds and exclusions? What happens when a system, user, or workload is designated critical?
- Reversibility and recovery: Which actions can be undone, by whom, and how quickly? What is the recovery procedure if an automated action disrupts a legitimate business process?
- Evidence and audit: Can you export alert context, investigation notes, model recommendations, human approvals, response actions, timestamps, and final dispositions?
- Performance definitions: How are “accuracy,” “resolved,” “response time,” “false positive,” and “hours saved” calculated? Ask for the measurement period, event denominator, exclusions, and results relevant to a comparable environment.
- Escalation and exceptions: Who is contacted when the provider cannot determine whether an event is malicious, when the customer does not respond, or when a containment step would affect a critical service?
- Coverage and change management: What happens when you add a new platform, change a data source, or remove an integration? Are there gaps in monitoring during onboarding or configuration changes?
- Customer references and scenarios: Ask for references from organizations with similar telemetry, regulatory obligations, and operating constraints. Have the provider demonstrate an ordinary alert, a false positive, a high-confidence threat, and a case where automation should stop and seek human approval.
A proof of concept should validate your actual integrations and operating rules rather than reward a polished dashboard or a single speed statistic. Define the test scenarios, expected evidence, approval boundaries, and success measures in advance; otherwise, providers may demonstrate different things under the same label.
Which model fits your security team?
An organization with a small internal security team may value a provider that absorbs routine triage and offers dependable human escalation. A mature security operations center may prefer granular controls, integration with its existing tools, and the ability to retain approval over disruptive actions. A company with strict data-residency or regional requirements may need to prioritize where analysts and telemetry are located, regardless of how much AI a provider uses.
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AI can make parts of MDR faster or more scalable, but the benefit depends on the quality of the signals, the actions permitted, and the provider’s governance. Human investigation and customer control remain central parts of a sound service evaluation.
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