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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsEvaluate an AI-powered managed detection and response (MDR) service by checking what it can see, how it investigates alerts, who may act during an incident, how it protects your data, and what its AI actually does. Ask every candidate for evidence of those capabilities, then test its operating model against the same incident scenarios. A dashboard or “AI-powered” label alone does not establish that a service can detect or contain threats in your environment.
Start with the service boundary: what will it monitor?
MDR depends on the signals a provider can receive and interpret. Before assessing detection claims, document which parts of your organization are in scope: business units, locations, devices, identities, cloud accounts, networks, applications, and other relevant systems. Identify excluded environments as carefully as included ones. A service cannot investigate activity it cannot observe.
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Ask the provider for a written coverage map, not just a list of integrations. For each source, record which data is collected, the integration prerequisites, who configures it, when collection begins, and how the provider will identify gaps or interruptions. A product may support an integration without your organization having deployed or configured it.
Check whether the telemetry is useful
CISA’s logging guidance identifies user activity, administrator actions, network traffic, application logins, and system events as useful records. It recommends enabling logs on servers, firewalls, endpoint devices, and cloud services. Use those categories as a starting point for asking which events your service can actually receive, rather than assuming a nominal connection provides complete visibility.
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For each source, request a sample inventory and a sample normalized event record. Check whether records include enough context to distinguish an account, device, application, action, and time. Ask how missing, delayed, malformed, or unexpectedly quiet data is surfaced to you. A useful service should make its blind spots visible, including sources that need customer configuration or are outside the contract.
- Endpoints: Which devices and operating environments are covered, and what happens when a device stops reporting?
- Identity and access: Which identity systems, authentication events, and administrative actions are in scope?
- Cloud, network, email, and applications: Which accounts and services are connected, and what events are collected from each?
- Locations and business units: Are remote users, subsidiaries, and separate environments included or excluded?
- Onboarding: Who installs, configures, and validates each integration, and how is successful data flow confirmed?
CISA summarizes the operational reason to ask who reviews these records: “Logs that go unanalyzed are useless.” The point is not to maximize log volume for its own sake. Establish which records analysts use, how gaps are detected, and what happens when visibility is insufficient for a confident investigation.
Follow one alert from signal to closure
A detection is only the start of an operational process. Ask a provider to walk through a representative alert from the underlying signal to the final case record. The walkthrough should show how an alert is triaged, what context is added, how analysts investigate it, how uncertainty is communicated, and what information you receive when the case is closed.
Questions to answer during the walkthrough
- What triggered the alert, and what supporting events can an analyst inspect?
- How does the provider separate suspicious behavior from expected or benign activity?
- Which steps are automated, and which require an analyst to review evidence?
- How are findings enriched with relevant account, device, network, or application context?
- When does a case become an incident, and who decides its severity?
- How can your team challenge a finding, add context, or correct an assumption?
- What evidence is retained and delivered, including the timeline, affected assets, actions taken, and closure rationale?
Ask how threat hunts are initiated and documented, too. A provider should be able to explain whether a hunt is triggered by a specific customer concern, an observed pattern, or another defined process, and how findings are shared. Do not treat a broad promise to “hunt for threats” as a substitute for learning what the work entails.
Request the provider’s own service measures, with definitions, denominators, time windows, exclusions, and data sources. Compare metrics only when the candidates define and calculate them in compatible ways. A headline response figure without its starting point, endpoint, scope, and exclusions does not tell you what will happen in your environment. The cited official guidance does not establish a universal MDR response-time target.
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Define who can act during an incident
Detection and response authority are different questions. Decide which actions the provider may take automatically, which require your approval, and what should happen if your designated contact cannot be reached. Put the agreed model in the contract and operating procedures; do not leave it to an informal understanding during an incident.
Separate advisory, pre-authorized, and approval-required actions
For each likely action, record whether the provider can recommend it, execute it under standing authorization, or act only after a named customer role approves it. Consider containment and remediation separately: an action that limits immediate risk may also interrupt legitimate business activity. Ask how the provider handles that trade-off and records the rationale.
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- What can the service do without contacting you, and under what conditions?
- Which actions require approval, and who is authorized to give it?
- What is the escalation path if the primary contact is unavailable?
- Is there a defined emergency process, and how are its use and consequences documented?
- What actions are outside the service’s authority or capability?
- How are actions, approvals, timestamps, and affected assets recorded for later review?
Also establish who owns remediation after containment. The provider may identify an issue or take an agreed response action while your organization remains responsible for restoring systems, correcting configuration, managing business communications, or deciding broader recovery priorities. Confirm that the handoff and ownership of each follow-up task are explicit.
CISA’s MSP and customer advisory recommends endpoint and network defense monitoring and that customers develop and exercise incident-response and recovery plans that include organizational stakeholders and roles. It is broad MSP/customer guidance, not a prescribed approval model for every MDR service. Use it to prompt a clear division of responsibilities and a response plan that your organization can exercise.
Assess the AI claim task by task
“AI-powered” does not identify a specific capability. Ask the provider to distinguish deterministic rules, statistical or machine-learning detection, and any generative-AI features. A service may use different techniques for different parts of its workflow, and a label alone does not tell you where AI is used or what evidence supports its output.
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Build an AI-use inventory
For each AI-assisted step, request a plain-language description of its intended use, the data it receives, the output it produces, and how people use that output. Ask for examples of what the system does when evidence is incomplete, conflicting, or outside the conditions for which it was evaluated.
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- Errors: How are false positives and missed detections reviewed, tracked, and addressed?
- Human oversight: When does an analyst review, override, or escalate an AI-generated result?
- Changes: How are updates to models, rules, or other AI-assisted features controlled and communicated?
- Monitoring: How does the provider look for drift or declining performance after deployment?
- Accountability: Who owns a decision made with AI assistance, and how can your team challenge it?
- Data use: Is customer data used for model training or service improvement, and what controls or choices apply?
Request documented evidence that answers these questions, not an assurance that the service is “accurate” or “autonomous.” Ask how an AI-assisted recommendation is distinguished from a confirmed finding in the case record, and whether that distinction remains visible to your analysts.
NIST’s AI Risk Management Framework is voluntary guidance intended to help incorporate trustworthiness across AI design, development, use, and evaluation. Its considerations include reliability, safety, security, accountability, transparency, explainability, privacy, and fairness. It is not a certification of an MDR service. The NIST AI RMF FAQs provide additional context on the framework.
If a provider uses generative AI, ask which tasks depend on it and how its outputs are reviewed before they influence customer-facing findings or actions. NIST’s AI 600-1 Generative AI Profile addresses risks including data privacy, information security, human-AI configuration, and third-party integration. Its guidance is relevant to generative-AI risk management; it does not establish that any particular MDR service uses generative AI.
Trace the data from collection through deletion
Security telemetry and incident records can contain sensitive information about users, systems, and business activity. Before onboarding, identify what the provider collects, where it is processed and stored, who can access it, and what happens to it when the service ends. Ask for answers that cover both routine telemetry and case data such as analyst notes, attachments, and investigation evidence.
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Questions for security, privacy, and procurement
- What event data, metadata, and case materials are collected, and which are optional?
- Where are data stored and processed, and how is cross-border handling addressed?
- Which provider staff and subcontractors can access customer information, and for what purposes?
- How is access controlled and audited?
- How long are logs and case records retained, and what determines the retention period?
- How can your organization export data or request deletion, including at termination?
- Can customer data be used for model training or service improvement, and what contractual terms govern that use?
Check that the answers align with your organization’s policies and compliance needs. CISA’s logging guidance recommends secure log storage and retention according to policies and compliance requirements. NIST’s AI risk materials also treat privacy and information security as risk areas. Neither source establishes how a particular provider stores or uses its customers’ data; verify those arrangements directly and capture them in procurement documents.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Make the operating relationship contractible
A service can have a sound technical approach and still fail your operational needs if contacts, responsibilities, or support arrangements are unclear. Review the proposed contract and service procedures together. The contract should reflect the actual coverage boundary and decision model you assessed, rather than relying on broad product descriptions.
Put these operating details in writing
- Included environments, telemetry sources, exclusions, onboarding tasks, and how coverage gaps are reported.
- Monitoring hours, support availability, severity definitions, escalation contacts, and communication channels.
- Customer and provider responsibilities for investigation, approvals, containment, remediation, and recovery.
- Incident notification obligations, the evidence supplied, and the process for updating the customer during an active case.
- Data collection, access, retention, subcontracting, processing locations, and export or deletion at termination.
- How changes to service scope, integrations, AI-assisted functions, or operating procedures are communicated.
- Transition assistance and continuity arrangements if the service ends or changes materially.
NIST AI 600-1 identifies responsibility, notification for serious incidents involving third-party data and systems, and service-level agreements addressing incident response, response times, and critical-support availability as considerations in generative-AI third-party contexts. Treat these as useful prompts for contract review, not as an MDR contract template or legal requirements. Have the appropriate security, privacy, legal, and procurement stakeholders review terms against your organization’s needs.
Compare candidates on the same assumptions
Use a consistent evaluation record for every candidate. Write down what the provider claims, the evidence it supplies, dependencies on your environment, and unanswered questions. A useful comparison focuses on operational fit rather than marketing terminology.
| Evaluation area | Record for each candidate |
|---|---|
| Coverage | Included assets and telemetry, exclusions, onboarding prerequisites, and visibility into collection gaps. |
| Investigation | Alert triage, analyst workflow, context added, threat-hunting process, case evidence, and customer ability to challenge findings. |
| Response authority | Advisory actions, pre-authorized actions, approval requirements, emergency handling, audit trail, and remediation ownership. |
| AI governance | Declared use cases, data inputs, evaluation evidence, limitations, human review, change control, monitoring, and customer data use. |
| Data protection | Access controls, processing and storage locations, retention, subcontractors, export, deletion, and training use. |
| Contract fit | Responsibilities, notification obligations, support availability, service measures and definitions, continuity, and exit terms. |
Do not rank providers from an “AI-powered” label or compare response metrics unless their definitions, populations, time windows, and exclusions match. If a candidate cannot provide evidence for a claim, record that as an open question rather than treating the claim as a verified capability. The official guidance cited here supplies procurement criteria, not named-provider performance comparisons, product tests, pricing, or an industry-wide benchmark.
Use the same scenarios to test the process
A structured walkthrough can reveal gaps that a feature list will not. Give each finalist the same scenario and ask it to narrate what its service would need to see, what it would do, and what your organization would receive. This is a buyer-led tabletop exercise, not a substitute for testing the provider’s deployed service in your own environment.
- Choose a scenario: For example, a compromised administrator account, suspicious endpoint behavior, cloud identity misuse, or a suspected ransomware event.
- Identify required visibility: Ask which telemetry and integrations the service would need, whether those sources are in your proposed scope, and how it would flag missing data.
- Trace investigation: Have the provider explain what would trigger an alert, how analysts would investigate, and what evidence would support or weaken the finding.
- Map decisions and authority: Ask who would be contacted, which actions could occur automatically, what needs approval, and what happens if contacts are unavailable.
- Follow communications: Establish the notification route, expected updates under the proposed service terms, the decision-maker, and how uncertainty is conveyed.
- Review the record: Ask what incident timeline, actions, evidence, and follow-up responsibilities you would receive after the case.
- Capture gaps: Record missing telemetry, unclear ownership, unsupported assumptions, and contract terms that need to change before onboarding.
CISA recommends that MSP customers develop and exercise incident-response and recovery plans. Use the walkthrough to see whether the provider’s proposed roles fit your own plan, and involve the staff who would actually make decisions or carry out recovery.
Make the procurement decision
Before selecting a service, require clear answers to five decision questions: Does it cover the systems and signals you need? Can you follow its investigation from alert to evidence-backed outcome? Are response powers and customer responsibilities explicit? Can the provider explain and govern each AI-assisted task? Do data handling and contract terms fit your organization’s requirements?
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Resolve material gaps before signing or onboarding. If visibility depends on integrations you have not configured, assign an owner and confirm the expected data flow. If response authority is ambiguous, settle it in writing. If an AI claim lacks an explanation of inputs, oversight, evaluation, or limitations, treat the capability as unsubstantiated for your decision. The strongest fit is the service whose scope, operating process, and accountability you can verify—not simply the one with the most expansive label.
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