[HUNT] ACTIVE_HYPOTHESIS_COUNT :: 14_RUNNING
[HUNT] DWELL_TIME_TARGET :: SUB_7_DAYS
[HUNT] TTP_CORRELATION_FEEDS :: 6_ACTIVE
[HUNT] HUNT_CADENCE_STATUS :: BIWEEKLY
[HUNT] ADVERSARY_PROFILE_LIBRARY :: 38_TRACKED
[HUNT] SIGMA_RULE_CONVERSIONS :: 412_ACTIVE
[HUNT] MEMORY_FORENSIC_SCANS :: SCHEDULED
[HUNT] DOMAIN_FRONTING_DETECTION :: MONITORED
[HUNT] LOLBIN_ABUSE_BASELINE :: ESTABLISHED
[HUNT] NEW_PERSISTENCES_FOUND :: 3_THIS_MONTH
[HUNT] HUNT_TO_DETECTION_RATE :: 31_PCT
[HUNT] THREAT_INTEL_FUSION :: LIVE
[HUNT] ADVERSARY_SIMULATION_CORR :: ACTIVE
[HUNT] HUNT_REPORT_CADENCE :: MONTHLY
[HUNT] ACTIVE_HYPOTHESIS_COUNT :: 14_RUNNING
[HUNT] DWELL_TIME_TARGET :: SUB_7_DAYS
[HUNT] TTP_CORRELATION_FEEDS :: 6_ACTIVE
[HUNT] HUNT_CADENCE_STATUS :: BIWEEKLY
[HUNT] ADVERSARY_PROFILE_LIBRARY :: 38_TRACKED
[HUNT] SIGMA_RULE_CONVERSIONS :: 412_ACTIVE
[HUNT] MEMORY_FORENSIC_SCANS :: SCHEDULED
[HUNT] DOMAIN_FRONTING_DETECTION :: MONITORED
[HUNT] LOLBIN_ABUSE_BASELINE :: ESTABLISHED
[HUNT] NEW_PERSISTENCES_FOUND :: 3_THIS_MONTH
[HUNT] HUNT_TO_DETECTION_RATE :: 31_PCT
[HUNT] THREAT_INTEL_FUSION :: LIVE
[HUNT] ADVERSARY_SIMULATION_CORR :: ACTIVE
[HUNT] HUNT_REPORT_CADENCE :: MONTHLY
Security Operations · Domain 03 · Tier 2
Continuous Proactive Threat Hunting
Hypothesis-driven human-led hunts that surface adversaries living inside your environment before automated detections trigger — reducing dwell time to days, not months.
The Case for Proactive Threat Hunting
Automated detections are calibrated for known threats — advanced adversaries specifically design their tradecraft to operate beneath detection thresholds.
Global median attacker dwell time — but 25th percentile is still 2+ months for stealthy APTs
[Mandiant M-Trends 2024]
Of threat hunt missions surface previously unknown attacker presence
[SANS Threat Hunting Survey 2023]
Of intrusions detected by external notification rather than internal controls — indicating systemic detection gaps
[Mandiant M-Trends 2024]
Proactive Threat Hunting vs. Alert-Only Detection
Alert-driven detection models work on a simple premise: known-bad behaviour triggers a rule, an alert fires, an analyst investigates. This model fails against advanced adversaries who invest specifically in evading rules — using living-off-the-land binaries (LOLBins), legitimate remote management tools, and slow low-volume C2 communication designed to fall below detection thresholds. The 31% hunt mission success rate from the SANS survey reflects systematic detection gaps that rule-based systems never close.
Vyomerc's threat hunting programme operates on adversary intelligence, not rules. Hunters develop hypotheses based on specific adversary TTPs relevant to your sector, then systematically search for indicators of those behaviours in your telemetry — from memory artefacts and scheduled task anomalies to subtle DNS beaconing patterns. Each confirmed hunt finding is converted into a new detection rule, compounding your detection library with every engagement.
Vyomerc Threat Hunting
Alert-Only Detection
Detection model
Hypothesis-driven human analysis targeting adversary TTPs invisible to automated rules
Rule-based alerting — blind to unknown and novel attack patterns
Adversary coverage
38 tracked adversary profiles inform hunt hypotheses specific to your sector and technology stack
Generic rule coverage; no adversary-specific focus
Dwell time impact
Biweekly hunt cadence reduces advanced persistent dwell time from months to days
Dwell time limited only by rule evasion capability of attacker
Detection improvement
Every hunt finding converted to a new SIGMA detection rule for automated future coverage
No feedback loop; gaps remain after every missed detection
Operational Workflow
How the Engagement Executes.
[PHASE_01]
Adversary Profiling & Hypothesis Development
Sector-specific threat intelligence analysis identifying the adversary groups, TTPs, and attack patterns most relevant to your organisation — forming the basis for hunt hypotheses.
[PHASE_02]
Data Source Validation
Assessment of available telemetry sources against hunt hypothesis requirements — identifying log gaps, sensor blind spots, and retention issues that would prevent hypothesis testing.
[PHASE_03]
Structured Hunt Execution
Systematic hypothesis testing using structured hunt methodologies across endpoint, network, and identity telemetry, with memory forensics for APT-class persistence techniques.
[PHASE_04]
Finding Conversion & Reporting
All confirmed findings documented as SIGMA rules for automated detection coverage, with executive hunt reports covering adversary activity identified and dwell-time reduction metrics.
[PHASE_01]
Adversary Profiling & Hypothesis Development
Sector-specific threat intelligence analysis identifying the adversary groups, TTPs, and attack patterns most relevant to your organisation — forming the basis for hunt hypotheses.
[PHASE_02]
Data Source Validation
Assessment of available telemetry sources against hunt hypothesis requirements — identifying log gaps, sensor blind spots, and retention issues that would prevent hypothesis testing.
[PHASE_03]
Structured Hunt Execution
Systematic hypothesis testing using structured hunt methodologies across endpoint, network, and identity telemetry, with memory forensics for APT-class persistence techniques.
[PHASE_04]
Finding Conversion & Reporting
All confirmed findings documented as SIGMA rules for automated detection coverage, with executive hunt reports covering adversary activity identified and dwell-time reduction metrics.
Capability Matrix
Technical Specification & Deliverables.
Hypothesis-Driven Methodology
Hunts are structured around specific adversary hypotheses derived from current threat intelligence — not random telemetry review — with documented methodology and measurable outcomes per mission.
LOLBin & Living-off-the-Land
Specialist focus on living-off-the-land techniques using Windows management tools, scripting engines, and remote administration software that evade traditional signature-based detection.
Hunt-to-Detection Pipeline
Every confirmed hunt finding is converted to a maintained SIGMA detection rule, creating a compounding detection improvement flywheel with each engagement cycle.
Hunt Engagement
Find the adversaries your rules will never see.
We conduct an initial threat hunt scope assessment, identifying hypothesis candidates from your sector threat landscape and available telemetry before commencing a pilot hunt.
[HUNT_OPERATIONS // TELEMETRY_RESTRICTED // ATT&CK_ALIGNED]