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Share the minimum telemetry an MDR provider needs to perform the monitoring, investigation, threat-hunting and response functions in your contract. That usually means selected endpoint events and alerts, threat indicators, and scoped identity, network, cloud and incident context—not every log, file or packet. Before connecting a feed, map each requested field to a security task, identify what sensitive information it reveals, and document how the provider and its AI features handle, retain and delete it.
Start with the security job, not a default log list
There is no universal “AI-enabled MDR dataset.” A provider’s requirements depend on the detections it promises, the systems in scope, the investigation workflow and the response authority you grant it. Ask the provider to explain, source by source and field by field, which monitoring, investigation, hunting or response function uses each item.
NIST SP 800-150 defines cyber threat information as information that helps an organization identify, assess, monitor and respond to cyber threats. Its examples include indicators of compromise, attacker tactics, techniques and procedures, suggested defensive actions and incident-analysis findings. Use those categories to define the outcome you need, then request only the data that supports it.
Cloud and endpoint guidance from CISA’s cloud interface reference architecture and CISA CDM technical capabilities shows possible sources such as network-flow records, DNS, VPN, IDS/IPS, cloud activity logs and endpoint event exports. These are examples for a provider-specific data map, not a mandatory feed for every MDR service.
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Telemetry that can have high security value
| Source or data class | Examples of fields or events | How it can help | Privacy and exposure considerations |
|---|---|---|---|
| Endpoint events and alerts | Process and trace events, endpoint alerts, event codes, configuration state, hostnames, device names, usernames, filenames and paths | Correlates suspicious execution, persistence, configuration changes and host activity during detection and investigation | Names, paths, usernames and device identifiers can identify people, systems or projects |
| Network and access telemetry | Source and destination IP addresses, DNS, VPN, IDS/IPS events, network-flow records and timestamps | Shows connections, unusual destinations, remote access and traffic patterns associated with an incident | IP addresses can imply rough location; timestamps and network data reveal user and business activity |
| Identity context | User IDs, account activity and authentication-related events | Links an event to an account, privilege level or access sequence | Stable identifiers remain correlatable, even when names are replaced with pseudonyms |
| Cloud and SaaS activity | Cloud activity logs, tenant identifiers, administrative events and audit logs | Detects changes to cloud resources, suspicious administration and cross-service activity | Tenant, project and resource names can expose organizational structure and sensitive workloads |
| Investigation content | Command lines, script content, geolocation, browsing metadata and related endpoint telemetry | Provides detail needed to reconstruct execution and determine attacker behavior | Shell commands, scripts and browsing content can contain secrets, personal data or confidential business information |
| Threat and incident context | Indicators of compromise, tactics, techniques, procedures, incident findings and recommended defensive actions | Improves triage, threat hunting, containment and lessons learned | Incident records may include sensitive business, legal or employee information |
A concrete example shows how broad a single service can become. Trellix’s MDR privacy data sheet lists trace, process, alert, event and configuration information, including IP and MAC addresses, filenames and paths, timestamps, hostnames and device names, usernames, system configuration state, event codes, geolocation, command lines, script content, source and destination IPs, endpoint telemetry and audit logs. The list describes that service’s disclosure and should not be treated as an industry-wide minimum. Read the Trellix MDR privacy data sheet.
Classify sensitive fields before transmission
Security usefulness does not make a field harmless. The NIST SP 1800-28 Volume B guidance warns that logging creates and moves additional information about users and that aggregation can reveal more than any individual log. It states that “the privacy impact of each log and the aggregation of logs must be considered.” An IP address may suggest a rough location; a device identifier can enable re-identification or correlation; and a timestamp can connect activity to a person’s work pattern.
Review these categories explicitly:
- People and devices: names, usernames, user IDs, MAC addresses, hostnames, device names and tenant identifiers.
- Location and activity: IP addresses, geolocation, timestamps, VPN records, browsing metadata and cloud resource names.
- Content: filenames, paths, command lines, script bodies, browser contents, file contents and values embedded in logs.
- Business context: incident notes, project names, customer identifiers, source-code paths and configuration details.
Pseudonymization is not the same as anonymity when the provider can correlate a stable token with other records. Ask what joins are possible across endpoints, identity systems, cloud tenants and historical data, and who can reverse or access those links.
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Minimize the feed without breaking detection
- Define the contracted use case. List the assets, identities and cloud environments the provider must monitor, plus the detections and response tasks it must perform.
- Build a field-level data map. For every source, record the event types, fields, collection frequency, destination, security purpose and sensitivity. Require the provider to explain why each requested field is necessary.
- Remove out-of-scope sources. Do not export systems, business units, geographies or applications that the MDR team is not responsible for.
- Filter or transform fields. Redact content, suppress unnecessary command arguments, remove file contents, or replace stable identifiers with pseudonyms when the investigation workflow still works.
- Test the reduced feed. Run agreed detection and investigation scenarios. Confirm that analysts can identify the host, account, timeline and action needed without restoring data that was intentionally excluded.
- Version the configuration. Record filters, transformations and exceptions. Reassess them whenever the provider adds a detector, changes an AI feature or expands the response scope.
Minimization must preserve the agreed security outcome. Removing a field that is genuinely required to distinguish two hosts or reconstruct a malicious process can create a blind spot; keeping it forever without a documented purpose creates unnecessary exposure.
Protect data while it moves and while the provider holds it
NIST SP 800-47 Rev. 1 says exchanged information needs protection before, during and after it moves between organizations, with protection commensurate with risk. Put the following terms in the service agreement and applicable data-processing terms rather than relying on a sales presentation:
- Data categories and exact telemetry sources covered by the service
- Permitted monitoring, investigation, support and security-improvement purposes
- Processing regions, international transfer mechanisms and location controls
- Provider, affiliate and subprocessor access, including privileged support access
- Retention by data type, backup retention, deletion timing and return or export format
- Encryption in transit and at rest, key-management responsibilities and access logging
- Incident notification deadlines and cooperation duties
- Customer audit, assurance-report and evidence rights where applicable
Check the current provider documentation and service settings. “Regional hosting,” “anonymous telemetry” or “no training” can describe only one feature, region or data class. Ask whether derived detections, embeddings, prompts, traces, analyst notes and de-identified records follow the same rule as raw events.
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Ask exactly how AI handles your telemetry
AI processing is feature- and provider-specific. Before enabling an AI analyst, summarizer, hunting assistant or automated action, identify every model and upstream service that receives raw telemetry, derived data, prompts, traces or outputs.
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Ask whether customer data is used to train or improve shared models, whether the choice is opt-in or opt-out, whether the setting covers derived data and human-reviewed examples, and whether the provider can change the policy without a new approval. Obtain the commitment in the contract or an incorporated data-use policy.
Inputs, traces and outputs
Determine whether prompts, session traces, model outputs, analyst feedback and error reports are stored. Microsoft’s Defender Agent 365 documentation, for its named capability, says agent observability traces may include session inputs and outputs depending on customer instrumentation. It lists agent attributes, user IDs, pseudonymized identifiers and tenant identifiers. The page states trace and session data may be retained for up to 30 days, while agent inventory and shared data may be retained for up to 180 days; it also describes regional storage based on tenant provisioning and says Microsoft does not use customer data to train AI models without user consent or to train generative-AI foundation models unless instructed by the customer. Those statements apply to that Microsoft capability, not automatically to another MDR service. Review Microsoft’s Defender Agent 365 data-handling documentation.
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Models, subprocessors and outages
Ask which model families, hosted APIs and subprocessors are involved, where each operates, what happens when an AI dependency is unavailable and how a provider isolates one customer’s data from another’s. A provider-authored eSentire AI buyer guide recommends these questions, along with asking about opt-in and opt-out controls and human oversight. Treat that guide as procurement advice, not independent verification of a control.
Auditability and human review
Require records showing which data an AI feature received, what it produced, which rule or model version was used, who approved an action and what changes followed. Establish a human review path for high-impact recommendations and a fallback process when the model is wrong, unavailable or produces an unsafe instruction.
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Set boundaries for automated response
Telemetry sharing and response authority are separate decisions. Define whether an action is advisory, analyst-approved or automatic. Specify which systems may be isolated, disabled, reconfigured or modified; which identities may be suspended; and which changes require customer approval.
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Document emergency permissions, time limits, rollback steps, notification recipients and a safe fallback if the provider or its AI cannot reach your systems. CISA’s endpoint materials describe response actions as governed by configured agency policy, reinforcing the need to configure permissions deliberately rather than granting broad authority by assumption.
Compare MDR providers on data governance
Use a written comparison matrix. The important differences are often in configuration and contract language rather than in the word “AI”:
| Comparison axis | Questions to document |
|---|---|
| Telemetry coverage | Which endpoint, identity, network, cloud and SaaS sources are supported? Can fields and event types be selected individually? |
| Minimization | Can you redact, pseudonymize, filter or exclude content before upload? Does detection still work with those controls enabled? |
| Processing geography | Where are raw events, backups, AI traces and support copies processed and stored? |
| Retention and deletion | What are the periods for raw telemetry, alerts, derived data, prompts, outputs, backups and post-termination deletion? |
| Subprocessors and models | Which companies, hosted models and APIs receive data, and how are changes announced? |
| Training and secondary use | Is customer data excluded from shared-model training by default? What opt-in, opt-out and contractual controls apply to derived data? |
| Access and assurance | How is privileged access approved and logged? What audit reports, testing evidence and incident notices are available? |
| AI and response control | Can AI features be disabled independently? Which actions require a human, and how are approvals and rollbacks recorded? |
| Performance evidence | What evidence supports detection, triage and response claims, and under what scope and conditions was it measured? |
A practical approval checklist
- The provider has mapped every requested field to a named security function.
- Out-of-scope systems and unnecessary content are excluded at the source or collector.
- Identifiers, locations, timestamps and browsing or command content have a documented handling decision.
- Encryption, access control, processing locations, subprocessors, retention, deletion and incident notice are contractual.
- AI models, upstream services, prompts, traces, outputs and training uses are identified.
- Customer-controlled disablement and opt-in or opt-out settings are tested.
- Automated actions, human approvals, emergency access and rollback are defined.
- A reduced-telemetry test confirms that agreed detections and investigations still work.
The defensible default is to send enough context to answer the provider’s contracted security questions, then stop. Revisit the map when your assets, threat model, provider features or legal obligations change.
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