# Threat Hunting Hypothesis Development
Threat hunting hypothesis development is the structured practice of forming explicit, testable premises about adversary behavior before conducting a hunt, then systematically validating or refuting those premises against collected data. A hypothesis in this context is not a guess. It is a precise, intelligence-grounded statement that predicts what observable evidence would exist in your environment if a specific threat actor technique were present.
This discipline sits at the core of proactive security operations. Rather than waiting for alerts to fire, threat hunters enter the environment with a defined question: "If APT29 is using OAuth application consent abuse to maintain persistent access in our Microsoft 365 tenant, what specific artifacts would we expect to find, and where?" The answer to that question drives every subsequent decision, from which data sources to query to which conditions count as confirmation.
Hypothesis development separates mature threat hunting programs from ad hoc data exploration. It creates reproducible, measurable hunts that generate lasting value regardless of outcome. A refuted hypothesis is not a failure; it is documented evidence that a specific adversary technique has not found purchase in your environment, which is genuinely useful information for risk posture decisions.
The hypothesis-driven model follows an eight-step cycle that transforms a raw intelligence signal into documented detection logic.
Step 1: Intelligence Trigger
Every hypothesis begins with a trigger, some piece of intelligence that raises a question worth investigating. Triggers come from several sources: a new Mandiant M-Trends report identifying techniques used against your vertical, a CrowdStrike Global Threat Report naming active threat groups in your industry, a US-CERT advisory about a recently observed TTP, a red team debrief exposing a technique your detection stack missed, or an ISAC alert from a peer organization. The trigger does not need to be conclusive evidence of compromise. It needs to be credible evidence that a specific threat is plausible given your environment and industry.
Step 2: Formulate a Testable Premise
The trigger gets refined into a precise, falsifiable statement. A well-formed hypothesis has three components: the actor or technique being tested, the specific behavior that would manifest in your environment, and the artifact or observable that would confirm or deny the behavior. A strong hypothesis looks like this: "APT29 leverages OAuth application consent grants to establish persistent delegate access in Microsoft 365 environments. If this technique is active in our tenant, we would observe service principal creations or consent grants issued to unfamiliar applications outside of our change management windows."
A weak hypothesis looks like this: "Maybe someone is doing something bad with OAuth." The difference is operationality. The strong version can be executed against specific data sources, and its results can be evaluated objectively.
Step 3: Identify Required Data Sources
Once the hypothesis is formed, the hunter catalogs exactly which data sources are needed to test it. For the OAuth example, this includes Microsoft Entra ID audit logs (specifically the AuditLogs table in Sentinel or the Entra audit trail in the portal), OAuth consent logs capturing delegated and application permission grants, service principal sign-in activity, and unified audit logs from Exchange Online and SharePoint to catch downstream access. This step is explicit, not assumed. The hunter lists each required log source by name.
Step 4: Data Availability Gap Analysis
Before a single query runs, the hunter determines whether the required data actually exists in the SIEM or data lake. This is a gap analysis. If Entra ID audit logs are not being forwarded to Sentinel, or if the retention window is shorter than the suspected dwell time for the technique being tested, the hunt cannot proceed as designed. The gap analysis produces one of three outcomes: the data is available and the hunt proceeds, the data is partially available and the hypothesis must be scoped accordingly, or the data is missing and the hunt is deferred pending a logging configuration change. That logging gap itself becomes a documented finding with remediation assigned to the detection engineering team.
Step 5: Build Queries
With confirmed data availability, the hunter constructs queries in the appropriate query language for the environment. For Microsoft Sentinel, this means KQL (Kusto Query Language). For Splunk, it means SPL (Search Processing Language). For endpoint telemetry, it may mean YARA rules for static file analysis or Sigma rules that can be transpiled across platforms. Good threat hunting queries filter aggressively to reduce noise, baseline against known-good activity to surface anomalies, and produce results that a human analyst can triage in a reasonable time window. Queries should be version-controlled from the moment they are written, regardless of whether they produce findings.
Step 6: Execute and Investigate
The hunt runs. Results are triaged by human analysts who evaluate each finding against what a legitimate business process would look like versus what adversary activity would look like. Not every hit is malicious; the investigator's job is to apply contextual judgment that the query cannot. This phase often generates follow-on queries as investigators pivot from an initial finding to adjacent data (for example, from an anomalous consent grant to the sign-in history of the service principal that received it).
Step 7: Evaluate the Hypothesis
At the conclusion of the hunt, the hypothesis is evaluated against one of three outcomes. Confirmed: the hypothesis was supported by evidence, and the technique appears to be present or was present historically. Partially confirmed: some indicators aligned but fell short of definitive confirmation, warranting continued monitoring. Refuted: the data was searched comprehensively and no evidence of the technique was found, which is itself a documented outcome. Each evaluation is recorded formally, not discarded when the hunt window closes.
Step 8: Feed Findings to Detection Engineering
This step is where threat hunting generates compounding value. Confirmed findings are translated into persistent detection rules: Sentinel analytics rules, Splunk correlation searches, or detection-as-code entries in a Git repository. Refuted hypotheses document which techniques currently have no observable footprint and inform future logging requirements. The hunt report, including the original hypothesis, methodology, queries used, and outcome, becomes part of the organization's institutional knowledge base.
Unstructured threat hunting is expensive and difficult to defend. When a hunter spends forty hours exploring data without a defined premise, there is no way to evaluate whether those forty hours were well-spent, no way to replicate the hunt six months later with updated data, and no way to communicate findings to leadership in terms that support budget decisions.
Hypothesis-driven hunting solves each of these problems. It creates a measurable program: hunts completed per quarter, hypotheses confirmed versus refuted, detection rules generated from hunt outcomes, logging gaps identified and closed. These metrics demonstrate the operational value of the program in language that security leadership and CISOs can communicate upward.
Beyond program management, the structured methodology produces better hunts. A hunter who begins with a precise question knows when to stop searching. A hunter who begins with no premise has no logical stopping point and often stops when time runs out rather than when the question is answered.
Hypothesis Sources by Category
MITRE ATT&CK is the most systematic source of hypothesis triggers. Every tactic and technique in the ATT&CK matrix represents a documented adversary behavior with real-world examples and data source recommendations. Hunters can build a hypothesis backlog by iterating through ATT&CK techniques relevant to their environment, identifying which have detection coverage and which do not, and prioritizing those without coverage.
Sector-specific threat intelligence provides relevance filtering. The financial services sector faces different threat actors than healthcare or critical infrastructure. ISACs (Information Sharing and Analysis Centers) publish sector-specific alerts and adversary profiles that make hypothesis development more targeted. A hypothesis informed by an FS-ISAC alert about techniques used against peer institutions in the past sixty days is immediately more relevant than one derived from a general framework entry.
Red team and penetration test reports are underutilized hypothesis sources. When a red team successfully uses a technique during an assessment, that technique should immediately generate a threat hunting hypothesis: "If the red team successfully used this technique, and it was not detected by our alert stack, is there evidence of this technique being used by external actors in our environment?"
Example Hypotheses Mapped to ATT&CK Tactics
Initial Access: "Threat actors targeting our sector are using phishing campaigns with HTML smuggling payloads. If this technique reached users in our environment, we would find ISO or ZIP files downloaded through browser processes, followed by execution of LNK files or embedded executables within sixty minutes."
Persistence: "Adversaries may establish persistence via scheduled task creation outside of known change windows. If this technique is in use, we would observe schtasks.exe or at.exe invocations by non-administrative accounts, or scheduled tasks created pointing to paths in user-writable directories."
Lateral Movement: "Adversaries may use SMB for lateral movement from initial access hosts. If active, we would observe unusual SMB connections from workstations to workstations (as opposed to workstations to servers), particularly for workstations that do not normally initiate SMB."
Exfiltration: "Adversaries may exfiltrate data through permitted cloud storage applications. If active, we would observe upload volumes to OneDrive, Dropbox, or Google Drive significantly above the ninety-day baseline for the initiating user, concentrated in short time windows."
Query Language Considerations
KQL for Microsoft Sentinel offers native support for time-series analysis, cross-table joins, and machine learning functions through the make-series and series-decompose operators, which are useful for behavioral baselining. SPL for Splunk provides powerful statistical commands including stats, eventstats, and streamstats for constructing baselines from historical data. Both platforms support Sigma rule ingestion, making it possible to write a hypothesis query once in Sigma and transpile it for multiple platforms.
Within the Planetary Defense Model, threat hunting hypothesis development is a core capability of the TID domain, Threat Intelligence and Defense. TID's canonical methodology is Predictive Defense Intelligence (PDI), with the operating principle: "See the threat before it sees you."
Hypothesis development is the operational mechanism by which PDI becomes more than a tagline. PDI requires that defenders not simply react to threats that have already materialized but actively project forward, using intelligence about adversary behavior to test the environment before damage occurs. A well-executed hypothesis-driven hunt is PDI in action: intelligence triggers a question, the question drives investigation, and the investigation either confirms a threat or closes a potential gap in the detection stack.
CDA treats threat hunting as intelligence collection, not just security operations. Confirmed hunt findings are threat intelligence about your specific environment. Refuted hypotheses are intelligence about your defensive coverage. Both feed the broader intelligence cycle that informs mission design across the TID domain and, through the Planetary Crisis Protocol (PCP), the coordinated response posture across all six PDM domains.
In the CDA campaign structure, threat hunting hypotheses align to the C-HARDEN and C-DRILL campaigns. C-HARDEN missions focus on proactive identification and closure of adversary footholds. C-DRILL missions exercise the hunter's ability to operate under compressed timelines with adversary-simulated pressure. Both campaigns require practitioners to demonstrate structured hypothesis development as a prerequisite for mission advancement.
Hypothesis-driven hunting produces reproducible, measurable results where ad hoc hunting produces anecdote. The eight-step cycle (trigger, hypothesis, data sources, gap analysis, query, execution, evaluation, detection engineering feedback) is not bureaucracy; it is the structure that allows a hunt to generate institutional value beyond the individual analyst who ran it.
Data availability gap analysis is not optional. Running a hunt against incomplete data produces misleading confidence. If the logs are not there, the hypothesis must be deferred until they are.
Every hunt outcome generates value. A confirmed hypothesis produces a detection rule. A refuted hypothesis produces documentation that a technique has no current footprint and identifies what logging is needed to ensure that remains detectable. A gap finding produces a logging remediation ticket. No hunt ends without a deliverable.
Hypothesis sources should be systematic, not opportunistic. A backlog built from MITRE ATT&CK coverage analysis, sector threat intelligence, and red team findings ensures that hunting effort is directed toward the most relevant threats rather than whatever topic was discussed last at a conference.
Threat hunting is intelligence collection. The output of a hunt program, taken in aggregate over quarters and years, constitutes a detailed map of adversary interest in your environment and your defensive coverage of their preferred techniques. That map is a strategic asset.