{"id":4954,"date":"2025-09-19T15:00:42","date_gmt":"2025-09-19T15:00:42","guid":{"rendered":"https:\/\/cybersecurityinfocus.com\/?p=4954"},"modified":"2025-09-19T15:00:42","modified_gmt":"2025-09-19T15:00:42","slug":"how-does-fidelis-ndr-use-machine-learning-to-detect-threats-earlier-and-respond-faster","status":"publish","type":"post","link":"https:\/\/cybersecurityinfocus.com\/?p=4954","title":{"rendered":"How Does Fidelis NDR Use Machine Learning to Detect Threats Earlier and Respond Faster?"},"content":{"rendered":"
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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.<\/span>\u00a0<\/span><\/p>\n

The gap between \u201calert created\u201d and \u201cincident contained\u201d widens when you can\u2019t 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.<\/span>\u00a0<\/span><\/p>\n

You accelerate detection and response with machine learning that understands normal, spots meaningful deviations, correlates signals across the kill chain, and drives automated actions. In practice, that means adopting an NDR<\/a> approach where models learn your traffic, surface high-fidelity anomalies, and prioritize what you must handle now.<\/span><\/p>\n<\/div>\n<\/div>\n

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Why does machine learning matter for NDR right now\u2014and what risks do you miss without it?<\/h2>\n<\/div>\n<\/div>\n
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1. You need detection that evolves faster than attacker tradecraft<\/h3>\n<\/div>\n<\/div>\n
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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. If you only look for known bad, you react too late. Machine learning in threat detection and response<\/a> learns normal behavior for your environment and flags departures as they <\/span>emerge.<\/span><\/p>\n<\/div>\n<\/div>\n

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\t\t\t\t\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>
\n\t\t\t\t\t\t\t\t\t\tYou gain coverage for novel exfil paths, unusual SaaS destinations, and rare protocol abuse that signatures ignore.<\/span><\/p>\n

\t\t\t\t\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>
\n\t\t\t\t\t\t\t\t\t\tYou shorten the \u201cunknown unknowns\u201d window by promoting truly unusual sequences into your queue.<\/span><\/p><\/div>\n<\/div>\n

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Pro tip:<\/span><\/span><\/strong><\/em> 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.<\/span><\/span><\/p>\n<\/div>\n<\/div>\n

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2. Encrypted and east-west traffic demand behavior, not just content<\/h3>\n<\/div>\n<\/div>\n
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You inspect less plaintext every quarter. TLS everywhere makes payload inspection harder, and east-west movement pivots inside your trust boundary. Network anomaly detection<\/a> with machine learning focuses on flow dynamics, session structure, metadata richness, and behavioral baselines even when content is opaque.<\/span><\/span><\/p>\n<\/div>\n<\/div>\n

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\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>
\n\t\t\t\t\t\t\t\t\t\tYou catch off-hours data bursts, abnormal handshake patterns, or atypical inter-service chatter.<\/span><\/p>\n

\t\t\t\t\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>
\n\t\t\t\t\t\t\t\t\t\tYou spot lateral movement when internal hosts communicate in ways your environment rarely sees.<\/span><\/p><\/div>\n<\/div>\n

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Pro tip:<\/span><\/span><\/strong><\/em> Build baselines per segment and per role. Treat a finance workstation chatting with an engineering build server as inherently suspicious, regardless of payload visibility.<\/span><\/span><\/p>\n<\/div>\n<\/div>\n

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3. Your SOC must prioritize with context, not volume<\/h3>\n<\/div>\n<\/div>\n
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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.<\/span><\/span><\/p>\n<\/div>\n<\/div>\n

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\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>
\n\t\t\t\t\t\t\t\t\t\tYou route decisive work to the front of the line and auto-suppress repetitive low-value noise.<\/span><\/p>\n

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\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>
\n\t\t\t\t\t\t\t\t\t\tYou cut MTTR because analysts start with enriched, contextualized alerts rather than raw logs.<\/span><\/p><\/div>\n<\/div>\n

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Pro tip:<\/span><\/span><\/strong><\/em> Tie model outputs to clear analyst actions (\u201cisolate host,\u201d \u201cre-auth user,\u201d \u201ccollect memory\u201d). Decision friction kills speed.<\/span><\/span><\/p>\n<\/div>\n<\/div>\n

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4. You need predictive threat detection to shrink dwell time<\/h3>\n<\/div>\n<\/div>\n
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Threats leave weak signals before damage. Predictive models forecast <\/span>likely next<\/span> steps\u2014exfil after staging, C2 after persistence\u2014and help you interdict earlier.<\/span><\/p>\n<\/div>\n<\/div>\n

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\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>
\n\t\t\t\t\t\t\t\t\t\tYou move from \u201cdetect and clean up\u201d to \u201cpredict and prevent.\u201d<\/span><\/p>\n

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\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>
\n\t\t\t\t\t\t\t\t\t\tYou reduce blast radius by containing during setup, not after loss.<\/span><\/p><\/div>\n<\/div>\n

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Pro tip:<\/span><\/span><\/strong><\/em> Feed closed-loop outcomes (true\/false positives) back into models weekly. Fresh labels keep predictions sharp.<\/span><\/span><\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

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Align Deep Visibility for
\nPost-Breach Detection
\nand Response<\/div>\n<\/div>\n<\/div>\n
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\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>
\n\t\t\t\t\t\t\t\t\t\tShift to Detection and Response<\/span><\/p>\n

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\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>
\n\t\t\t\t\t\t\t\t\t\tRich Metadata from NTA and EDR<\/span><\/p>\n

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\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>
\n\t\t\t\t\t\t\t\t\t\tRise of Deception Defense<\/span><\/p><\/div>\n<\/div>\n

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\n\t\t\t\t\t\t\t\t\tDownload Now<\/span>
\n\t\t\t\t\t<\/span>
\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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How do you operationalize machine learning in NDR without adding noise?<\/h2>\n<\/div>\n<\/div>\n
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1. Collect the right signal, then enrich it<\/h3>\n<\/div>\n<\/div>\n
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Start with broad network visibility<\/a> (north-south and east-west) and consistent metadata (JA3\/JA4 fingerprints, SNI, DNS, HTTP, TLS telemetry, <\/span>file<\/span> and session attributes). Enrich with identity, asset criticality, and data classification. You give models the context they need to separate benign anomalies from emerging threats.<\/span><\/p>\n<\/div>\n<\/div>\n

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\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>
\n\t\t\t\t\t\t\t\t\t\tPrioritize sources your analysts already trust.<\/span><\/p>\n

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\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>
\n\t\t\t\t\t\t\t\t\t\tNormalize early; consistent fields keep models stable.<\/span><\/p><\/div>\n<\/div>\n

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Checklist: <\/h4>\n<\/div>\n<\/div>\n
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\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>
\n\t\t\t\t\t\t\t\t\t\tNorth-south + east-west visibility <\/span><\/p>\n

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\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>
\n\t\t\t\t\t\t\t\t\t\tIdentity and role linkage <\/span><\/p>\n

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\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>
\n\t\t\t\t\t\t\t\t\t\tAsset criticality and data sensitivity tags <\/span><\/p>\n

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\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>
\n\t\t\t\t\t\t\t\t\t\tRetention tuned for seasonal patterns <\/span><\/p><\/div>\n<\/div>\n

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2. Baseline behavior by role, segment, and application<\/h3>\n<\/div>\n<\/div>\n
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Models must learn \u201cnormal\u201d per cohort: finance laptops, CI\/CD agents, database subnets, partner VPNs. You reduce false positives<\/a> by comparing like with like.<\/span><\/span><\/p>\n<\/div>\n<\/div>\n

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\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>
\n\t\t\t\t\t\t\t\t\t\tCapture seasonality (quarter-end spikes, code freezes).<\/span><\/p>\n

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\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>
\n\t\t\t\t\t\t\t\t\t\tTrack rare but legitimate flows and mark them as approved exceptions.<\/span><\/p><\/div>\n<\/div>\n

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Pro tip:<\/span><\/span><\/strong><\/em> Build allow-lists for scheduled transfers and maintenance windows so models ignore noise and spotlight out-of-band behavior.<\/span><\/span><\/p>\n<\/div>\n<\/div>\n

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3. Design model governance and drift control from day one<\/h3>\n<\/div>\n<\/div>\n
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You keep trust high by measuring precision\/recall, reviewing feature importance, and watching for drift. Establish thresholds for auto-containment vs. human review.<\/span><\/span><\/p>\n<\/div>\n<\/div>\n

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\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>
\n\t\t\t\t\t\t\t\t\t\tPublish model cards (purpose, inputs, limits) to your SOC runbook.<\/span><\/p>\n

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\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>
\n\t\t\t\t\t\t\t\t\t\tRetrain on a set cadence; hot-fix with incremental learning when patterns shift suddenly (e.g., a new SaaS rollout).<\/span><\/p><\/div>\n<\/div>\n

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Checklist:<\/h4>\n<\/div>\n<\/div>\n
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\t\t\t\t\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>
\n\t\t\t\t\t\t\t\t\t\tMetrics: alert quality, MTTR, analyst touch time<\/span><\/p>\n

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\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>
\n\t\t\t\t\t\t\t\t\t\tRetrain cadence and rollback plan<\/span><\/p>\n

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\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>
\n\t\t\t\t\t\t\t\t\t\tHuman-in-the-loop gates for destructive actions<\/span><\/p><\/div>\n<\/div>\n

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4. Embed models into repeatable SOC workflows<\/h3>\n<\/div>\n<\/div>\n
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Models that <\/span>don\u2019t<\/span> 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.<\/span><\/p>\n<\/div>\n<\/div>\n

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\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>
\n\t\t\t\t\t\t\t\t\t\tUse tiered responses by risk score.<\/span><\/p>\n

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\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>
\n\t\t\t\t\t\t\t\t\t\tLog every automated step for audit.<\/span><\/p><\/div>\n<\/div>\n

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Pro tip:<\/span><\/span><\/strong><\/em> Start with \u201cassistive automation\u201d (enrich, correlate, pivot) before \u201cactive automation\u201d (<\/span>contain<\/span>, kill). Expand as confidence grows.<\/span><\/span><\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

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A quick comparison to align teams<\/h2>\n<\/div>\n<\/div>\n
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\t\t\t\t\tAreaBefore ML-Driven NDRWith ML-Driven NDR\t\t\t\t<\/p>\n

\t\t\t\t\tTriageVolume-based, first-in-first-outRisk-ranked, context-richDetectionKnown-bad signatures onlyBehavioral + predictive patternsEast-WestSparse visibilityCohort baselines, lateral movement<\/a> cluesResponseManual, inconsistentPlaybook-driven, tiered automationLearningAd hocClosed-loop, weekly model tuning\t\t\t\t<\/p><\/div>\n<\/div>\n<\/div>\n<\/div>\n

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How does Fidelis NDR put machine learning to work so you detect earlier and respond faster?<\/h2>\n<\/div>\n<\/div>\n
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1. Deep Session Inspection plus behavior models: see the whole conversation, not just packets<\/h3>\n<\/div>\n<\/div>\n
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Fidelis NDR uses Deep Session Inspection (DSI)<\/a> to reassemble and decode full sessions across protocols, then applies behavior analytics to spot threats spread over multi-packet flows. 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.<\/span>\u00a0<\/span><\/p>\n

You speed investigations with session-level context, not isolated events.<\/span>\t\t\t\t\t\t\t\t<\/p><\/div>\n<\/div>\n

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Action:<\/span><\/span><\/strong> Tune policy to escalate DSI-flagged anomalies involving sensitive assets.<\/span><\/span><\/p>\n<\/div>\n<\/div>\n

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Fidelis DSI – Advanced Data inspection and Threat Detection Capabilities<\/div>\n<\/div>\n<\/div>\n
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\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>
\n\t\t\t\t\t\t\t\t\t\tContent Inspection<\/span><\/p>\n

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\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>
\n\t\t\t\t\t\t\t\t\t\tContent Identification<\/span><\/p>\n

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\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>
\n\t\t\t\t\t\t\t\t\t\tFull Session Reassembly<\/span><\/p>\n

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\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>
\n\t\t\t\t\t\t\t\t\t\tProtocol and Application Decoding<\/span><\/p><\/div>\n<\/div>\n

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\n\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t\t\tDownload the Datasheet<\/span>
\n\t\t\t\t\t<\/span>
\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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2. Multi-context anomaly detection: external, internal, protocol, data movement, and event<\/h3>\n<\/div>\n<\/div>\n
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Fidelis NDR\u2019s anomaly framework evaluates five contexts\u2014external north-south flows, internal east-west communications, application-protocol behavior, data movement patterns, and event correlation\u2014so you surface the right outliers in the right place. You reduce noise and reveal lateral movement and exfiltration routes earlier.<\/span><\/span><\/p>\n

External context:<\/strong> C2 patterns, unusual destinations.<\/span>\u00a0<\/span>Internal context:<\/strong> rare peer-to-peer links, privilege-pivot paths.<\/span>\u00a0<\/span>Protocol context:<\/strong> malformed or abused protocol behaviors.<\/span>\u00a0<\/span>Data movement:<\/strong> off-hours spikes, atypical repositories.<\/span>\u00a0<\/span>Event context:<\/strong> fusing rules\/signatures with behavior to raise confidence.<\/span>\u00a0\t\t\t\t\t\t\t\t<\/p><\/div>\n<\/div>\n

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Action:<\/span><\/span><\/strong><\/em> Review cohort baselines quarterly to keep contexts aligned with business changes.<\/span><\/span><\/p>\n<\/div>\n<\/div>\n

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3. Cyber terrain mapping, visibility, and faster detection<\/h3>\n<\/div>\n<\/div>\n
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Fidelis NDR maps your \u201ccyber terrain\u201d\u2014assets, relationships, and communication paths\u2014to 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 Fidelis<\/a> approach). You gain a prioritized view of what matters and catch risky behavior other tools miss.\u00a0<\/span>\u00a0<\/span><\/p>\n

You see which assets talk, when, and why\u2014so deviations stand out.<\/span>\u00a0<\/span>You use risk cues to triage faster and contain earlier.<\/span>\t\t\t\t\t\t\t\t<\/p><\/div>\n<\/div>\n

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Action:<\/span><\/span><\/strong><\/em> Tag sensitive data flows (finance, IP, regulated) so terrain analytics weight them higher.<\/span><\/span><\/p>\n<\/div>\n<\/div>\n

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4. Deception integrated with detection to raise signal quality<\/h3>\n<\/div>\n<\/div>\n
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Fidelis integrates deception<\/a> 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.\u00a0<\/span>\u00a0<\/span><\/p>\n

You convert \u201cmaybe\u201d into \u201cact now\u201d when decoys fire.<\/span>\u00a0<\/span>You gather intent evidence without monitoring intrusive content.<\/span>\t\t\t\t\t\t\t\t<\/p><\/div>\n<\/div>\n

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Action:<\/span><\/span><\/strong> Place decoys near high-value subnets and crown-jewel repositories to detect staging early.<\/span><\/span><\/p>\n<\/div>\n<\/div>\n

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5. Network DLP, sandboxing, and inspection depth for content-aware detections<\/h3>\n<\/div>\n<\/div>\n
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Beyond behavior, Fidelis NDR<\/a> applies network data loss prevention<\/a> and sandboxing alongside DSI. You unpack embedded files, analyze suspicious objects, and block exfil routes tied to sensitive content\u2014all while models score behavior around those transfers. This dual lens (content + behavior) improves precision and reduces false positives.\u00a0<\/span>\u00a0<\/span><\/p>\n

You detect data theft attempts even when attackers fragment or embed payloads.<\/span>\u00a0<\/span>You corroborate anomalous flows with content signals to justify containment.<\/span>\t\t\t\t\t\t\t\t<\/p><\/div>\n<\/div>\n

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Action:<\/span><\/span><\/strong> Align DLP<\/a> dictionaries with legal and compliance terms; revisit quarterly.<\/span><\/span><\/p>\n<\/div>\n<\/div>\n

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6. Automated, risk-tiered response across the platform<\/h3>\n<\/div>\n<\/div>\n
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As part of the Fidelis Elevate<\/a> approach, detections can trigger automated actions and orchestrated responses across your environment\u2014isolating devices, re-challenging identity, or collecting forensics\u2014so you compress time to contain without waiting on manual steps. You maintain auditability with clear playbooks and outcomes.\u00a0<\/span>\u00a0<\/span><\/p>\n

You eliminate lag between high-confidence detection and first containment.<\/span>\u00a0<\/span>You preserve evidence for post-incident analysis and model feedback.<\/span>\t\t\t\t\t\t\t\t<\/p><\/div>\n<\/div>\n

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Action:<\/span><\/span><\/strong><\/em> Start with alert-driven evidence collection, then graduate to containment for top-tier risk scores.<\/span><\/span><\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

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What should your first 90 days look like?<\/h2>\n<\/div>\n<\/div>\n
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\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>
\n\t\t\t\t\t\t\t\t\t\tWeek 1\u20132: Establish visibility and context Mirror north-south and east-west traffic. Onboard identity, asset tags, and data-classification sources. Define \u201ccrown jewels\u201d and sensitive pathways. <\/span><\/p>\n

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\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>
\n\t\t\t\t\t\t\t\t\t\tWeek 3\u20134: 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). <\/span><\/p>\n

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\n\t\t\t\t\t\t\t\t\t\tWeek 5\u20138: Automation with guardrails Introduce conditional access prompts for high-risk anomalies. Automate packet\/session capture on critical alerts. Add deception in high-value subnets. <\/span><\/p>\n

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\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>
\n\t\t\t\t\t\t\t\t\t\tWeek 9\u201312: 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. <\/span><\/p><\/div>\n<\/div>\n

\n
\n

Accelerate what matters, ignore what doesn\u2019t<\/span>\u00a0<\/span><\/p>\n

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. When you operationalize models with clear baselines, context, governance, and playbooks, your SOC moves faster with higher confidence. Fidelis NDR brings Deep Session Inspection, multi-context anomaly detection, cyber terrain mapping<\/a>, deception integration, content analysis, and orchestrated response together so you detect earlier and contain faster\u2014without drowning your analysts in noise. That\u2019s how you shift from chasing alerts to controlling outcomes.<\/span><\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

The post How Does Fidelis NDR Use Machine Learning to Detect Threats Earlier and Respond Faster?<\/a> appeared first on Fidelis Security<\/a>.<\/p>","protected":false},"excerpt":{"rendered":"

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.\u00a0 The gap between \u201calert created\u201d and \u201cincident contained\u201d widens when you can\u2019t separate real risk from noise. Adversaries exploit encrypted channels, low-and-slow exfiltration, and living-off-the-land tools […]<\/p>\n","protected":false},"author":0,"featured_media":4955,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2],"tags":[],"class_list":["post-4954","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news"],"_links":{"self":[{"href":"https:\/\/cybersecurityinfocus.com\/index.php?rest_route=\/wp\/v2\/posts\/4954"}],"collection":[{"href":"https:\/\/cybersecurityinfocus.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/cybersecurityinfocus.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"replies":[{"embeddable":true,"href":"https:\/\/cybersecurityinfocus.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=4954"}],"version-history":[{"count":0,"href":"https:\/\/cybersecurityinfocus.com\/index.php?rest_route=\/wp\/v2\/posts\/4954\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/cybersecurityinfocus.com\/index.php?rest_route=\/wp\/v2\/media\/4955"}],"wp:attachment":[{"href":"https:\/\/cybersecurityinfocus.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=4954"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/cybersecurityinfocus.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=4954"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/cybersecurityinfocus.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=4954"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}