Key Takeaways
- Machine learning enhances Fidelis NDR’s ability to detect advanced and previously unseen threats in real time.
- It improves network visibility by identifying anomalies across both encrypted and internal (east–west) traffic.
- Automated detection and response workflows help reduce false positives and speed up incident containment.
- By prioritizing real threats over noise, machine learning allows security teams to focus on what truly matters.
You face more signals than your SOC can triage and more lateral movement than your legacy rules can see. Signature-only controls miss new techniques, while manual triage slows response. Artificial intelligence, including machine learning, is now used to detect threats that evade traditional methods, enhancing the ability to identify sophisticated attacks.
The gap between “alert created” and “incident contained” widens when you can’t separate real risk from noise. Adversaries exploit encrypted channels, low-and-slow exfiltration, and living-off-the-land tools that look like normal activity. Missed weak signals become major incidents.
You accelerate detection and response with machine learning that understands normal, spots meaningful deviations, correlates signals across the kill chain, and drives automated actions. Automated threat detection reduces manual workload and enables faster response to incidents. In practice, that means adopting an NDR approach where models learn your traffic, surface high-fidelity anomalies, and prioritize what you must handle now.
Why does machine learning matter for NDR right now—and what risks do you miss without it?
1. You need detection that evolves faster than attacker tradecraft
Traditional rules detect what you already know. Adversaries iterate faster, mixing new command-and-control patterns, fileless techniques, and toolchains that blend into routine network use. This creates the ongoing challenge of evolving threats, requiring adaptive detection methods that can keep pace with attackers’ changing tactics. If you only look for known bad, you react too late. Machine learning in threat detection and response learns normal behavior for your environment and flags departures as they emerge.
- You gain coverage for novel exfil paths, unusual SaaS destinations, and rare protocol abuse that signatures ignore.
- You shorten the “unknown unknowns” window by promoting truly unusual sequences into your queue.
Pro tip: Use models that consider sequence and timing, not just point-in-time anomalies. A strange destination may be benign; a strange destination after a privilege change is not.
2. Encrypted and east-west traffic demand behavior, not just content
You inspect less plaintext every quarter. TLS everywhere makes payload inspection harder, and east-west movement pivots inside your trust boundary. Network anomaly detection with machine learning focuses on flow dynamics, session structure, metadata richness, and behavioral baselines even when content is opaque. Monitoring network traffic is essential for identifying suspicious activities and anomalies, especially as advanced threat detection increasingly relies on AI and machine learning.
- You catch off-hours data bursts, abnormal handshake patterns, or atypical inter-service chatter.
- You spot lateral movement when internal hosts communicate in ways your environment rarely sees.
Pro tip: Build baselines per segment and per role. Treat a finance workstation chatting with an engineering build server as inherently suspicious, regardless of payload visibility.
3. Your SOC must prioritize with context, not volume
Alert floods burn analysts. Machine learning for SOC operations ranks events by risk, using features like asset criticality, user role, data sensitivity, and sequence correlation. This directly improves security operations by streamlining analyst workflows, enabling faster detection and response to threats.
- You route decisive work to the front of the line and auto-suppress repetitive low-value noise.
- You cut MTTR because analysts start with enriched, contextualized alerts rather than raw logs.
Pro tip: Tie model outputs to clear analyst actions (“isolate host,” “re-auth user,” “collect memory”). Decision friction kills speed.
4. You need predictive threat detection to shrink dwell time
Threats leave weak signals before damage. Predictive models forecast likely next steps—exfil after staging, C2 after persistence—and help you interdict earlier. These predictive capabilities enable early detection of cybersecurity threats by analyzing data in real-time, allowing security teams to identify and respond to threats promptly before damage occurs.
- You move from “detect and clean up” to “predict and prevent.”
- You reduce blast radius by containing during setup, not after loss.
Pro tip: Feed closed-loop outcomes (true/false positives) back into models weekly. Fresh labels keep predictions sharp.
5. Reducing False Positives and Negatives: Raising Signal Quality with Machine Learning
In the evolving landscape of network security, the ability to distinguish real threats from harmless anomalies is essential. High rates of false positives can overwhelm security teams, while false negatives allow dangerous activity to slip through undetected. Machine learning transforms threat detection by dramatically improving the signal quality—helping you focus on what truly matters.
By continuously analyzing network traffic and user behavior, machine learning algorithms learn the difference between normal operations and suspicious activity. These models are trained on vast amounts of data, enabling advanced threat detection that adapts to your unique environment. As a result, your detection and response capabilities become more precise, reducing the noise that traditional security measures often generate.
Machine learning doesn’t just flag every deviation; it identifies potential threats by recognizing subtle patterns and correlations that would be missed by static rules. This means your security measures can more effectively identify potential threats, prioritize alerts, and minimize wasted effort on benign events. The outcome: fewer false positives, fewer missed attacks, and a security team empowered to respond faster and more confidently.
By leveraging machine learning in your threat detection systems, you enable your organization to stay ahead of emerging threats, improve overall network security, and ensure that your response capabilities are focused where they’re needed most.
- Reducing false positives
- Providing unmatched context
- Detecting hidden threats
How do you Operationalize Machine Learning in NDR without adding noise?
1. Collect the right signal, then enrich it
Start with broad network visibility (north-south and east-west) and consistent metadata (JA3/JA4 fingerprints, SNI, DNS, HTTP, TLS telemetry, file and session attributes). Enrich with identity, asset criticality, and data classification. Network traffic logs and data streams are foundational sources for building high-quality training data, enabling machine learning models to detect patterns, anomalies, and respond to evolving threats in real time. You give models the context they need to separate benign anomalies from emerging threats.
- Prioritize sources your analysts already trust.
- Normalize early; consistent fields keep models stable.
Checklist:
- North-south + east-west visibility
- Identity and role linkage
- Asset criticality and data sensitivity tags
- Retention tuned for seasonal patterns
2. Baseline behavior by role, segment, and application
Models must learn “normal” per cohort: finance laptops, CI/CD agents, database subnets, partner VPNs. You by comparing like with like. In supervised learning, labeled data is used to train models to distinguish between legitimate and suspicious behaviors.
- Capture seasonality (quarter-end spikes, code freezes).
- Track rare but legitimate flows and mark them as approved exceptions.
Pro tip: Build allow-lists for scheduled transfers and maintenance windows so models ignore noise and spotlight out-of-band behavior.
3. Design model governance and drift control from day one
You keep trust high by measuring precision/recall, reviewing feature importance, and watching for drift. It is crucial to validate models on unseen data to ensure they generalize well and accurately detect new or evolving threats. Establish thresholds for auto-containment vs. human review.
- Publish model cards (purpose, inputs, limits) to your SOC runbook.
- Retrain on a set cadence; hot-fix with incremental learning when patterns shift suddenly (e.g., a new SaaS rollout).
Checklist:
- Metrics: alert quality, MTTR, analyst touch time
- Retrain cadence and rollback plan
- Human-in-the-loop gates for destructive actions
4. Embed models into repeatable SOC workflows
Models that don’t trigger consistent action just add tickets. Wire detections into playbooks that: collect more evidence, re-challenge identity, isolate a host, or block an egress path.
- Use tiered responses by risk score.
- Log every automated step for audit.
Pro tip: Start with “assistive automation” (enrich, correlate, pivot) before “active automation” (contain, kill). Expand as confidence grows.
A Quick Comparison to Align Teams
| Area | Before ML-Driven NDR | With ML-Driven NDR |
|---|---|---|
| Triage | Volume-based, first-in-first-out | Risk-ranked, context-rich |
| Detection | Known-bad signatures only | Behavioral + predictive patterns |
| East-West | Sparse visibility | Cohort baselines, lateral movement clues |
| Response | Manual, inconsistent | Playbook-driven, tiered automation |
| Learning | Ad hoc | Closed-loop, weekly model tuning |
How does Fidelis NDR put machine learning to work so you detect earlier and respond faster?
1. Deep Session Inspection plus behavior models: see the whole conversation, not just packets
Fidelis NDR uses Deep Session Inspection (DSI) to reassemble and decode full sessions across protocols, then applies behavior analytics to spot threats spread over multi-packet flows. The system also analyzes network traffic to identify threats across entire exchanges. You catch exfiltration sequences, staged payload delivery, and protocol abuse that evade shallow inspection. This combination improves fidelity when traffic is complex or partially encrypted. You identify anomalies that manifest only across the entire exchange.
- You speed investigations with session-level context, not isolated events.
Action: Tune policy to escalate DSI-flagged anomalies involving sensitive assets.
- Content Inspection
- Content Identification
- Full Session Reassembly
- Protocol and Application Decoding
2. Multi-context anomaly detection: external, internal, protocol, data movement, and event
Fidelis NDR’s anomaly framework evaluates five contexts—external north-south flows, internal east-west communications, application-protocol behavior, data movement patterns, and event correlation—so you surface the right outliers in the right place. This multi-context approach helps close detection gaps that traditional methods, which often rely solely on behavioral analysis or signature matching, might leave open. You reduce noise and reveal lateral movement and exfiltration routes earlier.
- External context: C2 patterns, unusual destinations.
- Internal context: rare peer-to-peer links, privilege-pivot paths.
- Protocol context: malformed or abused protocol behaviors.
- Data movement: off-hours spikes, atypical repositories.
- Event context: fusing rules/signatures with behavior to raise confidence.
Action: Review cohort baselines quarterly to keep contexts aligned with business changes.
3. Cyber terrain mapping, visibility, and faster detection
Fidelis NDR maps your “cyber terrain”—assets, relationships, and communication paths—to assign risk and highlight likely attack routes. The platform emphasizes full visibility of data in motion and advertises materially faster post-breach detection (when deployed as part of the approach). You gain a prioritized view of what matters and catch risky behavior other tools miss. This enables proactive defense by anticipating and preventing attacks before they escalate through machine learning-enhanced endpoint security that learns from device usage patterns and detects vulnerabilities early.
- You see which assets talk, when, and why—so deviations stand out.
- You use risk cues to triage faster and contain earlier.
Action: Tag sensitive data flows (finance, IP, regulated) so terrain analytics weight them higher.
4. Deception integrated with detection to raise signal quality
Fidelis’s NDR platform integrates deception technology so you plant realistic decoys and honey tokens across the environment. When a user or process touches a decoy, you gain a high-confidence indicator and feed that signal into the same response engine. This removes ambiguity and deters probing. Deception technology helps detect insider threats, malicious actors, and unauthorized access attempts that might bypass traditional defenses.
- You convert “maybe” into “act now” when decoys fire.
- You gather intent evidence without monitoring intrusive content.
Action: Place decoys near high-value subnets and crown-jewel repositories to detect staging early. Deception also works alongside other security tools to create a comprehensive defense.
5. Network DLP, sandboxing, and inspection depth for content-aware detections
Beyond behavior, Fidelis NDR applies network data loss prevention and sandboxing alongside DSI. You unpack embedded files, analyze suspicious objects, and block exfil routes tied to sensitive content—all while models score behavior around those transfers. These capabilities support advanced malware detection, phishing prevention, and the identification of phishing attacks. This dual lens (content + behavior) improves precision and reduces false positives.
- You detect data theft attempts even when attackers fragment or embed payloads.
- You corroborate anomalous flows with content signals to justify containment, including detecting fraudulent transactions and malicious communications as part of the platform’s threat detection capabilities.
Action: Align DLP dictionaries with legal and compliance terms; revisit quarterly.
6. Automated, risk-tiered response across the platform
As part of the Fidelis Elevate approach, detections can trigger automated actions and orchestrated responses across your environment—isolating devices, re-challenging identity, or collecting forensics—so you compress time to contain without waiting on manual steps. You maintain auditability with clear playbooks and outcomes. Continuous monitoring is essential to ensure ongoing protection and rapid response to emerging threats.
- You eliminate lag between high-confidence detection and first containment.
- You preserve evidence for post-incident analysis and model feedback.
Action: Start with alert-driven evidence collection, then graduate to containment for top-tier risk scores.
What should your first 90 days look like?
- Week 1–2: Establish visibility and context:
- Mirror north-south and east-west traffic.
- Onboard identity, asset tags, and data-classification sources.
- Define “crown jewels” and sensitive pathways.
- Onboard security personnel and ensure they are trained to use the platform’s threat intelligence features for early detection of cyber threats.
- Week 3–4: Baselines and early wins:
- Build cohort baselines (role, segment, application).
- Whitelist scheduled maintenance transfers.
- Pilot auto-enrichment playbooks (e.g., whois, DNS history, identity lookups).
- Enable organizations to proactively defend against advanced persistent threats and potential security threats.
- Week 5–8: Automation with guardrails:
- Introduce conditional access prompts for high-risk anomalies.
- Automate packet/session capture on critical alerts.
- Add deception in high-value subnets.
- Support mobile devices and ensure coverage across the evolving threat landscape.
- Week 9–12: Governance and scale:
- Publish model cards and performance metrics.
- Expand playbooks to isolation for top 5% risk scores.
- Schedule quarterly baseline reviews and deception tune-ups.
- Use Fidelis Network for threat hunting and responding to cybersecurity threats.
Accelerate what matters, ignore what doesn’t
You win when you elevate signal and compress response. Machine learning threat detection pinpoints true anomalies, ranks them by business risk, and predicts next moves so you act before damage.
Fidelis NDR brings Deep Session Inspection, multi-context anomaly detection, cyber terrain mapping, deception integration, content analysis, and orchestrated response together so you detect earlier and contain faster without drowning your analysts in noise. That’s how you shift from chasing alerts to controlling outcomes.
Take control of your network defenses. Schedule a demo with Fidelis to see how you can detect earlier, respond faster, and outsmart evolving threats.
See why security teams trust Fidelis to:
- Cut threat detection time by 9x
- Simplify security operations
- Provide unmatched visibility and control
