Why custom detections still fall outside MDR coverage

At a glance

What this is: This article argues that MDR services typically do not fully cover customer-built detections, leaving the highest-context alerts in-house.

Why it matters: That matters to IAM and broader security teams because custom detections often encode identity-specific abuse, privilege misuse, and environment-specific signals that outsourced triage cannot safely interpret without local context.

By the numbers:

• In one side-by-side against a customer's own analysts, the platform matched their determinations on 99.8 percent of more than 12,000 investigations.

👉 Read Prophet's analysis of why custom detections fall outside MDR coverage

Context

Custom detections are rules written for a specific environment, usually to catch behaviour that vendor content does not model well. In practice, that means the alert often depends on business context, identity patterns, and application logic that only the internal team understands. The primary issue here is not alert generation but alert interpretation, especially when the signal reflects a local access pattern or a custom workflow.

For IAM and identity-heavy environments, the gap is especially visible around unusual authentication behaviour, service account misuse, and access paths that only make sense inside a particular identity model. MDR can cover standardized detections at scale, but it cannot automatically inherit the context behind internally authored rules. That makes custom detection ownership a governance problem as much as an operations problem.

The starting position described in the article is typical of mature enterprises, where some detections are internal by design and therefore remain outside shared-service coverage.

Key questions

Q: What breaks when custom detections are handed to MDR without local context?

A: The rule may still be visible, but the provider often lacks the environment-specific context needed to judge it correctly. That leads to shallow triage, false confidence, or excessive escalation back to the internal team. Custom detections are built around local business logic, so they need ownership, evidence, and response criteria that travel with the alert.

Q: Why do identity-heavy custom detections create a coverage gap in outsourced SOC models?

A: Because they often depend on local authentication patterns, privilege states, or service account behaviour that generic content does not understand. MDR can process the alert, but it cannot automatically inherit the intent behind the rule. When identity context is central, organisations need explicit handoff rules and named owners for investigation.

Q: How do security teams know whether a custom detection is actually being handled well?

A: Look for documented ownership, clear response criteria, preserved evidence, and consistent investigation outcomes. If the alert is regularly bounced back to the internal team, closed without explanation, or treated like generic noise, the service is only ingesting the signal, not covering it. Coverage should be measured by decision quality, not alert volume.

Q: Should organisations keep custom detections inside the SOC or outsource them to MDR?

A: If the rule depends on environment-specific identity or application context, keep investigative authority close to the internal team even if an MDR provider helps with triage. Outsourcing is most viable when the detection is standardised and the provider can interpret it without losing the rule’s original control purpose.

Technical breakdown

Why custom detections depend on local identity and environment context

Custom detections exist because generic rules cannot capture every relevant signal in a complex environment. They often encode knowledge about internal applications, expected authentication paths, privileged workflows, or unusual data movement that would look normal elsewhere. The value of the detection is not volume but specificity: it catches incidents that only make sense when you understand the organisation’s identity model, asset criticality, and normal operating patterns. That is why these rules are hard to evaluate outside the team that wrote them.

Practical implication: keep ownership of rule intent, test cases, and tuning with the team that understands the identity and application context.

Why MDR coverage stops at shared detections and tier-1 triage

Managed Detection and Response is built as a standardised service. Providers scale by running the same detection library across many customers, then triage the alerts generated by that library. Customer-written rules are different because they are not part of the provider’s common detection base, and they often require deeper interpretation than tier-1 handling can deliver. Even when a provider ingests those alerts, it may only forward them rather than investigate them with the context needed to reach a confident verdict.

Practical implication: define in contract and process whether customer-built detections are triaged, investigated, or simply forwarded by the MDR provider.

How investigation depth changes when the alert encodes business logic

A custom detection alert is often a question about whether a specific local pattern is legitimate or malicious. Answering it can require querying SIEM, EDR, identity provider, and cloud telemetry together, then reconstructing the intent behind the rule. That is a different job from checking a known-bad signature. Because the meaning sits in the local environment, the investigation must connect technical evidence to the original control objective, or the alert will either be closed too quickly or consume excessive analyst time.

Practical implication: build investigation runbooks that preserve the rule’s intent, evidence sources, and decision criteria before handing alerts to any external service.

Threat narrative

Attacker objective: The attacker’s objective is to operate inside the organisation’s unique identity and application logic without triggering a fast, accurate response.

1. Entry occurs when attackers exploit environment-specific identity or application behaviour that generic vendor detections do not model well.
2. Escalation follows when the custom rule captures subtle misuse of access, privilege, or data movement that requires local context to interpret.
3. Impact occurs if the alert is delayed, mishandled, or left with a team that does not understand the rule’s original control purpose.

NHI Mgmt Group analysis

Custom detections are a governance asset, not just a SOC workload. The value of a locally written rule is that it encodes the organisation’s own identity model, business processes, and risk tolerance. That also means the rule cannot be treated like generic MDR content without losing meaning. In practice, detection engineering and identity governance overlap here, because the team that understands privileged access and service account behaviour is often the only team that can judge the signal correctly.

Managed services do not remove ownership of high-context alerts. MDR is efficient at scale precisely because it standardises triage. The moment a detection depends on internal access patterns, the economics change and the alert becomes an owned exception. That is why teams need explicit accountability for custom rules, including who validates them, who tunes them, and who responds when they fire. Practitioners should treat this as a control boundary problem, not a service expectation problem.

Detection sprawl creates a hidden investigation debt. As custom rules expand, organisations accumulate more signals that require human judgment, but not all of them are equally portable to an outsourced model. Detection context gap: this is the specific failure mode where the alert exists outside the knowledge needed to interpret it. That gap is especially visible when identity data, privilege state, or application-specific rules are embedded in the detection logic. Practitioners should assume that every custom rule creates an investigation obligation unless it is explicitly operationalised.

Identity-heavy custom detections expose the limits of generic coverage. Rules that monitor unusual authentication paths, service account behaviour, or delegated access often matter most precisely because they are not generic. Generic MDR content can catch commodity threats, but it rarely understands the local identity architecture well enough to replace the original engineering intent. Practitioners should align detection ownership with identity architecture ownership wherever the rule depends on access context.

Evidence-driven coverage is the right test, not vendor promise. The question is not whether an MDR provider can see the alert. The question is whether it can investigate the alert with the same local context the internal team used to create it. That distinction should shape procurement, escalation design, and analyst handoff. Practitioners should evaluate detection coverage in terms of decision quality, not queue ingestion.

What this signals

Detection context gap: SOC programmes that rely on outsourced triage will increasingly need explicit exception handling for locally authored detections, especially where identity and privilege context determine whether an alert is benign or dangerous. That means alert routing, ownership, and escalation paths should be designed as part of the control itself, not bolted on after deployment.

The next maturity step is not simply adding more detections. It is deciding which signals require in-house judgment because they encode environment-specific identity behaviour, and which can safely live in shared-service workflows supported by NIST Cybersecurity Framework 2.0 and documented response criteria. Teams that cannot answer that question will keep paying for coverage they do not fully control.

For practitioners

• Document rule ownership and intent Assign each custom detection a named owner, a business rationale, the signals it depends on, and the exact conditions that justify closure or escalation. That documentation should travel with the rule into any shared-service workflow.
• Separate triage from investigation in contracts Define whether the MDR provider only receives custom alerts, performs tier-1 triage, or carries out full investigation. If the provider cannot interpret the rule’s local context, keep final disposition with the internal team.
• Build evidence packs for high-context alerts For each custom detection, predefine the SIEM queries, identity provider checks, and cloud telemetry needed to confirm or dismiss the alert. That reduces midnight guesswork and prevents unsafe bulk closure.
• Review custom rules through an identity lens Prioritise detections tied to unusual authentication, privilege changes, delegated access, and service account behaviour. Those rules are most likely to require local identity knowledge and the hardest to outsource safely.

Key takeaways

• Custom detections often sit outside MDR coverage because they depend on local identity and business context that shared services cannot safely infer.
• The real operational risk is investigation debt, where alerts are visible but not meaningfully understood or acted on with the right ownership.
• Practitioners should treat custom detections as governed assets, with clear ownership, evidence, and escalation design before outsourcing any part of the workflow.

Key terms

• Custom Detection: A custom detection is a rule written to identify a specific behaviour, pattern, or control violation that matters to one organisation. In browser security, it can target DOM activity, headers, or request flows so defenders can detect business-specific abuse instead of relying only on generic signatures.
• Detection Engineering: The discipline of designing, testing, and maintaining detection logic so it remains useful against real attacker behaviour. It covers telemetry selection, rule quality, false-positive management, and the operational workflow needed to keep alerts actionable.
• Tiered triage: A workflow that processes alerts in stages, using cheap deterministic logic first and reserving more expensive or nuanced analysis for cases that remain ambiguous. This reduces cost, lowers noise, and keeps human or AI attention focused where it is most useful.
• Investigation Debt: Investigation debt is the backlog of alerts that were closed, deferred, or partially reviewed without complete evidence. It behaves like technical debt in operations because it hides risk until a later incident or postmortem shows the missed context.

What's in the full article

Prophet's full article covers the operational detail this post intentionally leaves for the source:

• How Prophet frames the difference between a standard MDR library and customer-authored detections in day-to-day SOC operations.
• The article's full side-by-side coverage of where custom alerts stay with the internal team versus where MDR triage begins.
• More detail on the AI SOC investigation workflow and the evidence trail used to validate custom detections.
• The vendor's comparison criteria for teams evaluating MDR coverage against in-house detection engineering needs.

👉 Prophet's full article covers the SOC handoff problem, investigation burden, and AI SOC comparison in more detail.

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