Key Takeaways
Anomaly detection in XDR flags deviations from normal behavior, but an anomaly is a lead, not proof of malicious activity.
Network, endpoint, identity, and log telemetry together supply the context needed to judge a deviation.
Tuning, suitable baselines, and analyst validation keep false positives and alert fatigue manageable.
Buyers should test correlation, evidence quality, and response options on their own data.
Fidelis Elevate® combines behavioral anomaly detection, Active Threat Detection, Deep Session Inspection®, and deception for visibility and response.
Anomaly detection in XDR helps security teams identify deviations from expected user, device, and network behavior that may indicate a threat. Its value depends on what happens next: correlating the deviation with other security telemetry, investigating the evidence, and determining whether a response is warranted. XDR platforms can support these outcomes through different detection methods, including behavioral analysis, cross-layer correlation, and threat intelligence.
Below, we look at how anomaly detection improves threat visibility and response, where it falls short, and what to check when you compare XDR platforms. We also show how Fidelis Elevate® tackles related problems through weak-signal correlation across layers, Deep Session Inspection®, and deception. These are different mechanisms from baseline-based anomaly detection, and we treat them separately.
What does anomaly detection in XDR add beyond rule-based detection?
Signatures, rules, and indicators of compromise match what you already know about, and you should keep them. OMB’s M-26-14 logging memo lists monitoring for known indicators and monitoring for anomalous system or user activity as two separate baseline objectives[1]. Signatures and rules can identify known patterns, while anomaly detection can flag deviations that predefined detection logic may not capture.
Anomaly detection builds a picture of typical behavior for a user, device, or workload from historical data, then scores departures from it. That can be as plain as a threshold on outbound volume or as involved as machine learning algorithms across many variables. Unsupervised methods need no labeled attacks, but they can’t tell a new benign event from a new malicious one[4]. Platforms differ in the methods they run.
An anomaly only says “rare.” A finance analyst working from an airport, a backup job that saturates a link, and an attacker staging data can look identical. Context separates them. When you evaluate a platform, ask which of these methods drive which detections, and what evidence each one produces for the analyst.
How does anomaly detection improve threat visibility across security layers?
Network traffic shows who talked to whom and over what. Endpoint data shows which process did it. Authentication events show which identity was involved. M-26-14 names the same minimum set for answering incident questions: identity, source and destination details with protocols and ports, object and data events, privilege changes, infrastructure changes, and security tool findings.
Cross-layer data matters because one layer rarely tells the whole story. The NICT survey sketches a common intrusion chain of phishing, credential theft, lateral movement, privilege escalation, staging, and exfiltration. Any single alert in that chain can look unremarkable, and the sequence is the signal. Ask each vendor which telemetry its platform actually ingests and how it’s stored.
IBM’s Cost of a Data Breach 2026 report puts the average time to identify and contain a breach at 247 days[3]. Visibility has to close that gap.
What do point, contextual, and collective anomalies mean for an evaluation?
TypeSecurity exampleWhat you need to confirm it
PointOne host sends far more outbound data in an hour than it ever hasDestination and the process responsibleContextualAn admin account signs in at an hour and from a place it never hasRole, change records, what happened nextCollectiveShare enumeration, then new credentials on a second server, then an external transferA single cross-layer timeline
For buyers, the key question is whether the platform can explain why an event was flagged and supply the context needed to validate it. Isolated anomalies can generate noise, while related events across multiple layers can provide a stronger basis for investigation.
Which threats does anomaly detection help expose?
CISA’s cyber decoy guidance notes that many organizations struggle to detect adversaries who use legitimate credentials, native tools, and living off the land techniques to discover systems, move laterally, and reach data[2]. These attacks can evade signature-based detection, making behavioral evidence and cross-layer context particularly valuable.
ScenarioWhat may look unusualWhat you still need
Lateral movementA workstation authenticates to servers it has never contactedThe account and process, and whether an admin tool was used legitimatelyAccount compromiseA user account signs in from an unfamiliar place, then reaches resources outside its roleAuthentication outcomes and travel or change recordsSuspicious network trafficSteady, low-volume connections to a rare destinationReputation data and the process that opened the connectionUnexpected endpoint behaviorAn office application spawns a command shellThe parent and child process chain and related connectionsSensitive data access or exfiltrationAn account reads far more records than usual, then a large transfer leavesThe data’s classification, the destination, and authorizationMalware or an unrecognized threatA new binary with no reputation behaves oddlySandbox results and whether it appears on other hosts
Every row is a hypothesis. What follows depends on the evidence, your policy, and the permissions your tools hold.
How does an anomaly become a detection and response action?
In practice the path runs like this. You set a baseline, flag a deviation, and enrich it with telemetry, identity, threat intelligence, and related security events. You then correlate it with other evidence, rank it by risk, and investigate. Only then do you respond, based on evidence, policy, permissions, and the response capabilities you have. Afterward you review the outcome and adjust the baseline or detection logic. Few environments automate every step.
Keep four things separate when you assess a platform. Alert generation says something deviated. Investigation support supplies evidence. Response recommendations suggest an action. Automated response runs that action without an analyst. The NICT survey finds the evidence doesn’t yet support fully autonomous AI decision-making for security-critical work, so most teams will want policy limits and analyst approval on high-impact actions.
Why do anomalies create false positives, and how do teams manage them?
Benign change produces anomalies routinely: a new workload, a month-end batch job, a migration, or a patch window. A baseline built on a short window treats anything rarer than the window as odd, and normal behavior drifts. The NICT survey notes that models drift and that models trained in one security operations center often don’t transfer to another[4].
Base rates make this difficult. A field measurement cited in that survey found only about 0.01% of daily alerts in one large environment were tied to true attacks, and it links alert fatigue to high-recall detection meeting finite analyst time.
What helps:
Judge each deviation against role, time, and asset, not a global average.
Rank by severity combined with asset criticality, entity context, and threat intelligence. The survey warns that teams lacking an asset inventory or usable threat intelligence are unlikely to benefit from advanced prioritization.
Tune on a schedule. OMB’s logging maturity model ties higher maturity to detections that are routinely evaluated and tuned[1].
Keep an analyst between a deviation and any consequential action.
None of this removes false positives, and nothing guarantees finding unknown threats.
What should security teams evaluate in an XDR solution’s anomaly detection?
Treat each row as something to ask the vendor to show you, not describe.
CriterionAsk the vendor to show you
TelemetryWhich network, endpoint, identity, cloud, and log sources are ingested, and what is visible in encrypted or containerized trafficCorrelationOne detection tied to alerts from other layers on the same asset or accountExplainabilityThe signals behind a detection, in order, with evidenceFalse-positive handlingHow baselines update, what you can tune, and how analyst verdicts feed backPrioritizationHow detections are ranked and how many steps a typical investigation takesResponseWhich actions are recommended or automated, and what integrations and permissions they needDeployment and dataThe deployment model, how long data stays searchable, who can access it, and privacy and regulatory requirements
Two points deserve weight. For retention, M-26-14’s federal baseline of six months searchable and a year retrievable is a useful reference, even though it binds only agencies[1]. For effectiveness, the NICT survey found just 9 of 87 core studies were evaluated on real operational data, and it advises treating published results as upper bounds[4]. A test on your own telemetry beats both vendor claims and research.
How does Fidelis Elevate support threat visibility and response?
Fidelis Elevate is our open and active XDR platform. It combines Fidelis Network® for network detection and response, Fidelis Deception®, and endpoint detection and response from Fidelis or a third-party EDR platform. You can use the products together or on their own. CommandPost, delivered as an appliance or a service, stores data and metadata for retrospective analysis and threat hunting.
Here is how that maps to the criteria above.
Telemetry
Deep Session Inspection® analyzes traffic across all ports and protocols and gives far more content and context than netflow. It reaches nested files, encrypted traffic, and ephemeral containerized workloads, with real-time decryption. Fidelis Elevate also maps the environment and classifies managed and unmanaged assets. Fidelis Network adds data loss protection, which bears on the sensitive data scenarios above.
Behavioral anomaly detection
We also offer machine-learning-based behavioral anomaly detection, including analysis of external traffic, internal traffic, application protocols, data movement, and detection events. The questions in the table above apply here as they do to any anomaly signal: ask us to show the evidence behind a flagged deviation and how it is validated.
Correlation and explainability
Active Threat Detection correlates weak signals from Fidelis Network, Deception, Endpoint, and Sandbox alerts into conclusions mapped to MITRE ATT&CK. Each one shows the attack stage, the files and systems involved, the techniques, and a description of every supporting signal, so an analyst can see why it was raised. Weak-signal correlation works on a different principle from behavioral baselining. It assembles individually unremarkable signals into an evidence-based conclusion instead of scoring departures from typical behavior.
Deception
Cloud deception and Active Directory deceptive objects change the terrain an attacker sees. CISA describes decoys as assets that look like legitimate systems, accounts, or data but exist to distract adversaries, detect their presence, or support intelligence collection, and says they can help defenders detect post-compromise behavior earlier. A decoy is placed by design, so a touch on it doesn’t depend on first learning what normal looks like. In Fidelis Elevate, deception alerts are among the inputs Active Threat Detection correlates.
Identify and neutralize threats faster
Gain full visibility across your attack surface
Automate security operations for efficiency
Response
Fidelis Elevate supports real-time analysis and response automation, and once an attack sequence is identified, defenders can automate responses through scripts and playbooks. Active Threat workflows give analysts evidence and guidance for investigation and remediation. Out-of-the-box integrations and an API connect Fidelis Elevate to SIEM, SOAR, threat intelligence, packet broker, EDR, and security service edge products. Which actions run automatically in your environment depends on your policies, permissions, and integrations.
Frequently Asked Questions
How is anomaly detection different from signature-based detection in XDR?
Signatures match known patterns, such as a file hash or a known bad domain. Anomaly detection compares activity to a baseline and flags departures, even for patterns nobody has seen. Signatures give firmer matches on known threats. Anomalies give leads that need validation. Both belong in a layered approach.
Can anomaly detection identify previously unknown threats?
It can surface behavior nobody has described yet, but it can’t promise to. A threat that closely mimics normal activity may not deviate enough, and an unfamiliar benign event looks the same as an unfamiliar malicious one until someone investigates.
Does anomaly detection require machine learning?
No. Statistical baselines and thresholds can flag deviations, and rules can encode expected behavior on stable systems. Machine learning can model many variables at once, with added work on tuning, drift, and explainability. Ask which method drives which detection.
Does Fidelis Elevate use anomaly detection?
Fidelis Elevate brings together multiple threat detection methods. Fidelis Network uses behavioral machine learning and network traffic analysis to identify deviations from normal activity, including unusual internal communication, unexpected data movement, and suspicious protocol behavior. Fidelis Elevate’s Active Threat Detection complements these capabilities by correlating signals from network, endpoint, deception, and other supported security sources to provide broader context for investigation and response.
How should we test anomaly detection during an XDR evaluation?
Use your own telemetry and two or three scenarios from your threat model. Check what evidence each detection provides, how long an investigation takes, how false positives are handled, and what a response needs from your tools. CISA recommends testing and refining decoy operations through threat emulation, red teaming, or purple teaming, and that approach works for detection testing too.
References
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