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Cisco 300-220 CBRTHD is a current CCNP Cybersecurity concentration exam for Conducting Threat Hunting and Defending using Cisco Technologies for Cybersecurity. Cisco describes the exam around threat-hunting fundamentals, threat modeling, threat-actor attribution, hunting techniques and processes, and the outcomes that turn a hunt into better detection and mitigation. Unlike reactive alert handling, threat hunting begins with the possibility that malicious activity may already exist without having triggered a reliable detection.
The exam therefore rewards structured curiosity. Hunters form hypotheses, choose data, test for evidence, refine the search, document findings, and feed new knowledge back into defensive controls. It sits beside 300-215 CBRFIR as a different CCNP Cybersecurity concentration and connects to the 350-201 cybersecurity core. Within Cisco certifications, 300-220 is the proactive threat-hunting concentration rather than an incident-response exam.
Security controls can miss activity because signatures are absent, logging is incomplete, behavior looks legitimate in isolation, or the attacker deliberately avoids known indicators. Hunting asks a targeted question about possible malicious behavior and searches for evidence that normal detection may not have elevated.
A hunt should not be a random search through logs. It needs a hypothesis, scope, data sources, method, stopping condition, and a way to capture the result. Otherwise analysts can spend large amounts of time finding anomalies that have no security meaning.
The Threat Hunting Maturity Model explains why data and process matter. Organizations differ in how much hunting they can perform. Some rely mainly on vendor indicators; more mature teams develop hypotheses from internal telemetry, automate repeatable searches, and continuously convert successful hunts into new detections.
Maturity is not measured by the number of tools purchased. Reliable data collection, analyst skill, repeatable process, feedback, and organizational support determine whether hunts can be conducted consistently and whether their findings improve defense.
File hashes are easy for adversaries to alter, while infrastructure and tactics can be more costly to replace. The Pyramid of Pain helps hunters think about detections that force an attacker to change behavior rather than simply recompile a file.
Candidates should still use low-level indicators when they are useful. The model is not an instruction to ignore hashes or addresses; it is a reminder to seek behavioral patterns and relationships that remain valuable after individual indicators expire.
Frameworks such as MITRE ATT&CK, CAPEC, the Cyber Kill Chain, TaHiTI, and PASTA provide different ways to describe adversary behavior, weaknesses, attack progression, and hunting process. The hunter should select the model that clarifies the question rather than forcing every investigation into the same framework.
MITRE ATT&CK is especially useful for translating a suspected technique into expected data. If the hypothesis involves credential access, persistence, or command execution, the hunter can ask which endpoint, identity, process, or network artifacts should exist if that technique occurred.
Threat intelligence helps prioritize where to look but does not replace local evidence. External reporting can identify actor techniques, malware families, infrastructure, targeted sectors, and recent campaigns. That context can generate hypotheses and prioritize systems, but an organization still needs its own telemetry to determine whether the behavior occurred internally.
Hunters should assess source reliability, recency, relevance, and whether an indicator or behavior fits the environment. Blindly searching every published IOC creates noise and can distract from threats that are more plausible for the organization's technology and exposure.
Threat actors reuse tools and infrastructure, copy one another's techniques, and deliberately create false signals. Attribution therefore depends on multiple observations such as infrastructure history, tooling, timing, targeting, operational patterns, and intelligence context.
For most defenders, the immediate priority is containment and improvement rather than naming an actor. The hunter should state the confidence level and evidence behind any attribution so operational decisions do not depend on a dramatic but weak conclusion.
Native operating-system tools, endpoint telemetry, PowerShell, event logs, file metadata, persistence locations, and memory evidence can reveal activity that did not generate a conventional malware alert. Process trees and command lines are particularly useful because legitimate tools can become suspicious when executed by the wrong parent, user, or context.
The hunter should establish what is normal for the endpoint population before labeling every uncommon command malicious. Baselines, peer comparison, asset role, and user behavior help separate true anomalies from rare but legitimate administration.
Network hunts expose communication patterns across many systems. Packet captures, NetFlow or similar telemetry, DNS, proxy records, firewall data, and network analytics can reveal beaconing, unusual destinations, lateral movement, scanning, data staging, or command-and-control behavior. Network evidence is valuable because it can connect multiple endpoints into one campaign.
Encryption limits payload inspection but not all analysis. Timing, volume, destination reputation, certificate information, DNS behavior, flow direction, and peer relationships can still support a hypothesis when combined with endpoint and identity data.
SIEM and analytics platforms let hunters search across hosts and time, but poorly designed queries can return huge result sets or hide important edge cases. Analysts should start with a clear field or behavior, test the query on known examples where possible, and document filters so another hunter can reproduce the result.
Successful queries should often become reusable content. Converting a one-time search into a detection, scheduled analytic, dashboard, or documented hunting procedure allows the organization to benefit after the individual hunt ends.
Automation and AI can accelerate hunting but can also scale mistakes. Automation can enrich indicators, correlate events, execute saved queries, score anomalies, or orchestrate data collection. Machine-learning techniques can highlight behavior that differs from a baseline. These capabilities are useful when analysts understand the data and limitations behind them.
A model or automated score is not proof of malicious activity. Biased training data, environment changes, missing telemetry, and poorly chosen features can generate false conclusions. Hunters should validate high-impact findings with direct evidence before recommending disruptive action.
A hunt that finds malicious behavior may lead to containment, eradication, credential resets, blocks, or a formal incident response. A hunt that finds no compromise can still reveal logging gaps, weak hypotheses, missing visibility, or an opportunity to improve detections.
Cisco's blueprint emphasizes outcomes because the purpose is not merely analytical activity. Teams should document what was searched, what was found, which gaps remain, what mitigation is recommended, and what detection or process change should follow.
Mapping a finding to ATT&CK can show which tactics and techniques are observed and which data sources support them. It can also help identify blind spots where the organization has neither telemetry nor a detection for behaviors that matter.
The mapping is useful only when supported by evidence. Assigning technique IDs without explaining the observed behavior creates the appearance of rigor while making the report harder to use. Hunters should connect the framework label to the specific event pattern that justified it.
Threat detection and hunting reinforce one another. Detection engineering converts known suspicious behavior into scalable monitoring, while hunting deliberately looks for activity not yet covered well. Threat detection and analytical defense reinforce the same habit: form a hypothesis, collect evidence, test it, and turn confirmed behavior into better detection.
The important editorial connection is the concept: a successful hunt should teach the detection program something. If the same behavior can recur undetected tomorrow, the organization has not captured the full value of the investigation.
Candidates should work through scenarios in which a hypothesis is generated from intelligence or an anomaly, mapped to expected behaviors, translated into endpoint and network queries, tested against evidence, and concluded with a written outcome. The exercise should record both findings and data gaps.
This makes the frameworks and tools easier to remember because each has a purpose in the hunt. The exam still requires knowledge of Cisco technologies and terminology, but the enduring skill is being able to move from uncertainty to a defensible search, then turn the result into stronger defensive capability.
A hunt should have a defined scope and stopping condition. Without timeboxing or clear evidence thresholds, analysts can keep expanding a hypothesis indefinitely and consume resources without improving confidence. The team should state which population, time window, behaviors, and data sources are in scope, what would count as supporting or weakening the hypothesis, and when the hunt should be escalated into incident response or closed with documented gaps.
Hunt metrics should measure defensive learning rather than raw query volume. Useful outcomes include new detections, validated data sources, discovered visibility gaps, shortened investigation paths, confirmed false assumptions, and mitigations that reduce future exposure. Counting the number of hunts alone can reward activity without impact.
The strongest feedback loop connects hunting to detection engineering. When a hunt identifies a repeatable behavior, analysts should decide whether it can become a scheduled analytic, correlation rule, endpoint detection, network signature, or documented triage procedure. That conversion allows a one-time investigation to improve continuous monitoring and gives future hunts a higher starting point.
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