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Start with the security outcome, not a generic log bundle
Managed detection and response (MDR) works by collecting evidence, finding suspicious activity, helping an analyst investigate it and, when authorized, taking a response action. The right data set is therefore the smallest one that reliably supports the functions you purchased. There is no industry-wide “AI-enabled MDR” minimum feed. One service may need endpoint process events and identity context; another may rely more heavily on network, cloud or identity-provider telemetry.
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NIST 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, defensive actions and incident-analysis findings. The same guidance recommends setting sharing goals, scoping the activity and establishing distribution rules before exchanging information. See NIST SP 800-150.
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Telemetry that commonly has security value
CISA materials describe examples such as network-flow records, DNS, VPN, IDS/IPS and cloud-activity logs, along with endpoint event export and configured response actions. They are examples for building a provider-specific data map, not a universal checklist. The relevant CISA references are the cloud interface reference architecture and CDM technical capabilities.
| Data category | Examples of useful records | What it can support | Ways to narrow exposure |
|---|---|---|---|
| Endpoint events | Process, alert, event and endpoint telemetry; host and device identifiers; event codes | Malware detection, process-chain investigation, host scoping and escalation | Limit collection to in-scope devices and event types; filter unrelated applications and systems |
| Network and access activity | Source and destination IP addresses, DNS, VPN, IDS/IPS and network-flow records | Connection analysis, command-and-control investigation, unusual access and lateral-movement detection | Define monitored networks and time windows; avoid payload content when flow or metadata is sufficient |
| Cloud activity | Cloud activity logs and related audit records | Investigation of account, configuration and resource changes | Scope to tenants, subscriptions, accounts and services covered by the agreement |
| Identity context | Usernames, user IDs and authentication or audit events | Attribution, impossible-travel or account-abuse investigation and incident escalation | Use stable pseudonymous identifiers where analysts can still correlate events; keep a controlled re-identification key internally |
| Threat intelligence and case context | Indicators of compromise, tactics, techniques, procedures, defensive actions and incident-analysis findings | Threat hunting, prioritization, detection tuning and response decisions | Share only indicators and case details relevant to the provider’s scope and current incidents |
| Configuration and inventory state | System configuration state, device names, hostnames, filenames and paths | Exposure assessment, asset scoping and interpretation of endpoint events | Exclude unmanaged assets and redact path segments that reveal confidential project or customer names when they are not needed |
A provider disclosure illustrates how broad a feed can become. Trellix’s 2025 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 document describes these fields as supporting investigation and escalation; it is a description of that service, not an industry standard. Read the Trellix MDR Privacy Data Sheet and ask another provider to produce an equivalent field list.
Classify fields before they leave your environment
Identifiers and correlation data
Names, usernames, user IDs, hostnames, device names, IP addresses, MAC addresses, filenames, paths and timestamps can identify a person or reveal where they work. A pseudonym is not automatically anonymous: if the provider can correlate it with other records, it still carries identifying context. Decide whether the investigation needs the original value, a consistent token or only a category such as “privileged account.”
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Location and behavioral metadata
IP addresses can imply rough location. Geolocation, VPN records, DNS requests, browsing metadata and timestamps can reveal where a user was, which systems they accessed and when. NIST’s NCCoE warns that logging creates and moves additional information about users, and that aggregation can reveal more than any one log. Its guidance says, “The privacy impact of each log and the aggregation of logs must be considered.” See NIST SP 1800-28 Volume B and NIST SP 1800-29 Volume B.
Content-bearing fields
File contents, script bodies, shell command lines and browsing contents can expose credentials, personal information, source code, regulated records or confidential business plans. Establish whether the provider needs the content itself or only a hash, event type, command category, process name, destination or other metadata. If content is necessary for a defined investigation, specify the approved sources, access conditions and deletion schedule rather than enabling unrestricted collection.
Incident and case material
Incident notes, screenshots, forensic exports and analyst findings can contain more context than routine telemetry. Mark the systems, people and customers covered by the case; remove unrelated records; and define who may view the material. Keep a separate internal record of any mapping needed to turn pseudonymous identifiers back into names.
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A field-level process for deciding what to send
- Write the use case. List the exact services: continuous monitoring, threat hunting, malware investigation, cloud audit review, incident escalation, containment or recovery support. Include the assets, identities and regions in scope.
- Request a data map. Require the provider to name every source, field, transformation, destination and downstream service. Ask which fields are mandatory, optional, generated by the provider or collected only when an incident is opened.
- Mark sensitivity. Flag user and device identifiers, IP and MAC addresses, filenames and paths, timestamps, geolocation, command lines, scripts, browser activity and content. Consider the risk created when several sources are joined.
- Minimize the feed. Exclude out-of-scope systems, filter fields, redact content and pseudonymize stable identifiers where the investigation workflow still works. Retest detections and case timelines after each reduction.
- Document the transformation. Record hashing or tokenization methods, key ownership, redaction rules, sampling, time windows and exceptions. Make sure investigators can still distinguish one host or account from another when correlation is required.
- Validate with a controlled pilot. Send representative events from a non-production or tightly bounded segment. Confirm alert fidelity, investigation usefulness, analyst access and the behavior of any automated action before expanding collection.
- Approve the final matrix. Have security, privacy, legal and system owners sign the source-and-field list, purposes, regions, retention periods and response permissions. Reapprove it when a provider adds a feature, model or subprocessor.
Questions AI adds to the provider review
“AI-enabled” can mean automated correlation, analyst assistance, a summarization feature, a classification model or an agent that can call tools. Each feature may receive different inputs and create different retained records. Ask the provider to identify every model, AI feature, upstream service and subprocessor that receives raw telemetry, derived data, prompts, traces or outputs.
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- Data separation: Are your records segregated from other customers’ data during storage, retrieval, model evaluation and troubleshooting? Who can access cross-customer systems?
- Retention by feature: How long do raw events, agent traces, prompts, outputs, case notes and backups persist? Does deletion from the visible console also remove copies used for safety review or disaster recovery?
- Geography and transfers: Where are each data type and model processed and stored? Which transfer mechanism and regional restrictions apply to your organization?
- Human oversight: Which recommendations require analyst approval? How are prompts, model versions, decisions, overrides and approvals logged for later review?
- Failure handling: What happens if a model, upstream service or connectivity path is unavailable, produces an unsafe result or is compromised? Is there a documented manual fallback?
A provider-authored eSentire buyer guide suggests asking about the models and upstream services behind the AI, disruption and incident handling, segregation from other clients’ data or training, opt-in and opt-out controls, and human oversight of AI actions. Treat those as procurement questions, then verify the answers in binding terms and current technical documentation: eSentire AI buyer guide.
Product documentation can be precise without being universal. Microsoft’s Defender Agent 365 page says agent observability traces may contain session inputs and outputs when the customer enables that instrumentation, along with agent attributes, user IDs, pseudonymized identifiers and tenant identifiers. It states retention of up to 30 days for trace and session data and up to 180 days for agent inventory and shared data, with regional storage based on tenant provisioning. It also says customer data is not used to train AI models without user consent and is not used to train generative-AI foundation models unless the customer instructs Microsoft to do so. Those statements apply to the named capability and should not be generalized to another MDR service. Check the current page and terms at Microsoft Defender Agent 365 data handling.
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Put exchange and lifecycle controls in the contract
NIST SP 800-47 Rev. 1 states that exchanged information needs protection before, during and after it moves between organizations, with protection commensurate with risk. Use the service agreement, data-processing terms and security exhibits to specify:
- the data categories, approved purposes and prohibited secondary uses;
- encryption and authentication for collection, transfer, storage and administrative access;
- processing and storage locations, cross-border transfer arrangements and regional restrictions;
- provider personnel access, privileged support access, logging and multifactor authentication;
- subprocessors, model providers, notification of changes and your objection or termination rights;
- retention for each data type, backup handling, deletion deadlines, return format and portability assistance;
- breach and security-incident notification timelines, cooperation duties and evidence preservation;
- audit reports, independent assessments, customer audit rights where applicable and a way to verify deletion.
Do not rely on a short “no training” statement without defining what it covers. Clarify whether it includes prompts, outputs, derived detections, de-identified records, service-improvement analytics, support tickets and subprocessors.
Define what the provider may do in your environment
Telemetry access and response authority are separate decisions. A service may recommend isolation, disable an account, block an address, kill a process, change a cloud policy or modify an endpoint. For every action, record whether it is advisory, analyst-approved or automatic; the systems and accounts it can affect; the conditions that trigger it; the approval path; and the emergency fallback if the provider or model is unavailable.
CISA’s EDR material treats response actions as governed by configured agency policy. Apply the same principle internally: begin with read-only investigation where practical, test containment actions on a limited asset group, and require explicit approval before granting broad write or isolation permissions.
Compare providers on evidence, not the “AI” label
| Comparison axis | Questions to document |
|---|---|
| Telemetry scope | Which endpoint, identity, network, cloud and case sources are supported? Can you configure fields and collection triggers? |
| Minimization | Can you redact, filter, tokenize or disable content-bearing fields without losing the contracted detection? |
| Processing geography | Where are raw and derived data processed and stored, and what transfer terms apply? |
| Retention and deletion | What are the periods for events, traces, cases, backups and derived data? How is deletion verified? |
| Subprocessors and models | Which companies, models and upstream services receive telemetry or outputs, and how are changes disclosed? |
| Training and secondary use | Is customer data excluded by default? What opt-in, opt-out and contractual restrictions apply to derived data? |
| Auditability | Can you review access, prompts, model decisions, analyst approvals, automated actions and incident records? |
| Customer control | Can you disable an AI feature, pause a source, revoke credentials or require approval for response actions? |
| Performance evidence | Are detection, response or automation claims supported by defined methods, scope and conditions rather than slogans? |
Trellix documentation describes on-premises and SaaS deployment paths, regional clouds and subprocessors, while Microsoft’s named capability documents trace content, retention and geographic-storage behavior. Those examples demonstrate why field-level and feature-level comparison matters; neither establishes a universal MDR architecture.
Operational checklist before production collection
- Map every source and field to a monitoring, investigation, hunting or response task.
- Identify personal, confidential, regulated and content-bearing fields before enabling the connector.
- Set collection filters, pseudonymization or redaction and test that detections still work.
- Confirm encryption, access controls, administrator logging and multifactor authentication.
- Record processing regions, subprocessors, models, retention, deletion and portability commitments.
- Obtain an explicit answer on training, evaluation, service improvement and secondary use.
- Limit response permissions, define approval thresholds and test the manual fallback.
- Review the data map after major product, model, subprocessor or regulatory changes.
The practical answer
Send scoped endpoint, identity, network, cloud and incident telemetry that demonstrably improves the MDR functions you bought. Remove unnecessary content, preserve only the correlation needed for investigation, and treat identifiers and metadata as sensitive even when they look pseudonymous. AI does not remove the need for least-privilege collection; it makes model inputs, derived data, retention, subcontractors, training use and automated actions additional contract and governance questions.
Quick Recap
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