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Threat Intelligence & Detection Engineering

Detecting threats your tools
were never configured to find

Most enterprise security stacks contain the capability to detect sophisticated threats. Most are not configured to. Detection rules sit at vendor defaults, threat intelligence is consumed but not operationalised, and your SIEM generates thousands of alerts that your analysts have quietly learned to ignore. We fix that — systematically, measurably, and without requiring new tools.

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Platforms We Work With

Microsoft Sentinel SIEM / SOAR — KQL detection & automation
Splunk Enterprise / SOAR SPL detection rules & playbook design
CrowdStrike Falcon EDR policy, custom IOA & threat hunting
SentinelOne Singularity Detection tuning & STAR rules
IBM QRadar / LogRhythm Rule library & use case migration
Elastic SIEM / Google Chronicle Detection-as-code & pipeline design
The Detection Gap

Your tools are capable.
Your configuration is not.

The detection gap is not a tooling problem in most enterprises. It is a configuration, prioritisation and operationalisation problem. Your SIEM has ingested terabytes of log data. Your EDR has visibility across every endpoint. Your threat intelligence feeds are delivering fresh indicators daily. And your analysts are still triaging hundreds of low-fidelity alerts that bear no relationship to the actual threats targeting your sector.

The gap between what your stack is capable of detecting and what it actually detects is where adversaries operate. It is maintained not by a lack of tools, but by a lack of structured detection engineering — the discipline of designing, testing, tuning and maintaining detection logic as a deliberate practice, not as an afterthought.

We close that gap. We map your detection coverage against the MITRE ATT&CK framework, identify where you are blind, design use cases for the techniques most relevant to your threat model, and operationalise threat intelligence into detection logic that actually runs.

  • Know exactly which attack techniques your current stack can and cannot detect
  • Replace default vendor rules with use cases built for your environment
  • Reduce false positive volume without reducing genuine detection coverage
  • Operationalise threat intelligence into running detection logic
  • Build a detection engineering practice that improves continuously

The Detection Reality — Industry Benchmarks

76% of enterprise SIEM rules are never triggered — ever
58% of alert volume is false positive or duplicate noise
12 days Average dwell time before detection in enterprise environments
40% MITRE ATT&CK coverage in a typical untuned enterprise SIEM
3.2× Faster MTTD after structured detection engineering engagement
85% Reduction in false positive volume achievable through tuning
⚠️

The adversaries targeting your sector have mapped their techniques against the detection gaps common in your platform stack. Your detection logic needs to reflect that — not the generic use cases your vendor shipped three years ago.

What We Do

Four detection engineering
disciplines — one integrated practice

Effective detection is not a single activity. It is a continuous practice across four interconnected disciplines — each of which we execute as a structured, documented service.

🗺️

MITRE ATT&CK Coverage Mapping

We map your current detection capability against the MITRE ATT&CK framework — identifying which techniques you can detect with confidence, which you detect poorly and which you are completely blind to. We then cross-reference your coverage gaps against the tactics and techniques known to be used by threat actors targeting your sector, providing a risk-ranked priority list for new use case development.

Includes
Full ATT&CK matrix coverage assessment across your SIEM and EDR
Sector-specific threat actor technique mapping
Coverage heatmap for security leadership reporting
Prioritised use case development backlog
⚡

Detection Use Case Development

We design and implement detection use cases tailored to your environment, threat model and technology stack. Each use case is built against a structured specification — defining the technique being detected, the data sources required, the logic implemented, the expected true positive rate and the triage runbook for analysts. Every rule is validated in your environment before production deployment.

Includes
Structured use case specification and peer review
Detection logic development in your native SIEM language
Pre-production validation and false positive baselining
Analyst triage runbook for each deployed use case
🔕

Alert Tuning & Noise Reduction

We conduct a systematic analysis of your alert pipeline — identifying the rules generating the highest false positive volumes, the suppression logic that is over-broad, the data sources delivering low-quality signals and the triage workflows that are amplifying noise rather than reducing it. We address each root cause directly, rather than applying blanket suppression that reduces both noise and genuine detections together.

Includes
Alert volume analysis by rule, data source and severity
False positive root cause classification
Tuning implementation with before/after measurement
Alert quality scorecard for ongoing monitoring
📡

Threat Intelligence Operationalisation

Most organisations consume threat intelligence but don't operationalise it. Feeds deliver indicators into a platform that generates alerts nobody acts on, because the context, confidence and relevance information required to triage them hasn't been built into the detection logic. We integrate your threat intelligence feeds into your detection platform — transforming indicators into actionable, contextualised detections that your analysts can triage with confidence.

Includes
Threat intelligence feed audit and relevance assessment
Indicator operationalisation into SIEM detection logic
Confidence and context enrichment pipeline design
Sector-specific threat actor tracking programme
MITRE ATT&CK Framework

Typical enterprise detection coverage
before a GadgetAccess engagement

The bars below represent average detection coverage across each ATT&CK tactic in a typical enterprise SIEM — before detection engineering. Most organisations are well-covered in Execution and Persistence (where vendors focus their default rules) and significantly exposed across the lateral movement, discovery and exfiltration tactics where sophisticated adversaries actually operate.

Typical coverage before engagement
Coverage gap — adversary operating space
Initial Access
55% avg. coverage
Execution
70% avg. coverage
Persistence
65% avg. coverage
Privilege Escalation
48% avg. coverage
Defence Evasion
32% avg. coverage
Credential Access
42% avg. coverage
Discovery
28% avg. coverage
Lateral Movement
22% avg. coverage
Collection
30% avg. coverage
Command & Control
38% avg. coverage
Exfiltration
25% avg. coverage
Impact
45% avg. coverage

The tactics with the lowest average coverage — Defence Evasion, Discovery, Lateral Movement and Exfiltration — are precisely the phases where a sophisticated adversary spends the most time before the point of maximum impact. They are also the phases that generate the least noise in a default-configured SIEM, which is why they go undetected for an average of 12 days.

A GadgetAccess detection engineering engagement prioritises coverage uplift in these high-risk, low-coverage tactics first — using your actual threat model and your existing platform, not additional tooling.

Platform Coverage

We work in your stack —
not around it

We don't recommend new platforms to solve detection problems caused by poor configuration of existing ones. Every detection engineering engagement is executed within your current technology stack, using the platforms your team already operates.

SIEM

Security Information & Event Management

Detection rule development, KQL / SPL / YARA-L authoring, data source onboarding, alert tuning and correlation rule optimisation across leading enterprise SIEM platforms.

Microsoft Sentinel Splunk IBM QRadar LogRhythm Elastic SIEM Google Chronicle Exabeam
EDR / XDR

Endpoint & Extended Detection & Response

EDR policy configuration, custom detection rule development, threat hunting query libraries and behavioural detection tuning to reduce noise while improving coverage across endpoint telemetry.

CrowdStrike Falcon SentinelOne Microsoft Defender XDR Darktrace Cybereason Vectra AI
SOAR

Security Orchestration, Automation & Response

Playbook design and development for automated triage, enrichment and response workflows — reducing analyst handling time for high-volume, lower-complexity alert types to recover SOC capacity.

Microsoft Sentinel SOAR Splunk SOAR Swimlane D3 Security ServiceNow SecOps Tines
Threat Intelligence

Threat Intelligence Platforms

Feed integration, indicator operationalisation, threat actor tracking and intelligence-led detection use case development — connecting strategic and tactical intelligence to your SIEM detection logic.

Recorded Future MISP ThreatConnect Mandiant Advantage CrowdStrike Intel
Cloud Security

Cloud Detection & Posture

Cloud-native detection across AWS, Azure and GCP environments — covering misconfiguration detection, identity-based threat detection and cloud workload protection use case development.

AWS Security Hub Microsoft Defender for Cloud Google SCC Prisma Cloud Wiz
Detection as Code

Detection Engineering Pipeline

For mature security programmes, we design and implement a detection-as-code pipeline — bringing version control, automated testing and CI/CD deployment discipline to your detection rule management practice.

Sigma Rules GitHub / GitLab Detection-as-Code Automated testing CI/CD deployment
How We Work

From coverage assessment
to production detection in weeks

A GadgetAccess detection engineering engagement follows a structured four-phase approach — from assessing your current coverage baseline through to deploying validated use cases in production. Most initial engagements complete within four to six weeks.

1
Week 1

Detection Baseline Assessment

We assess your current detection coverage across your SIEM and EDR, map it against the MITRE ATT&CK framework and analyse your alert pipeline for volume, fidelity and triage workload. We establish your sector threat actor profile to inform prioritisation.

2
Week 2

Use Case Prioritisation & Design

We prioritise the detection use cases that will deliver the greatest coverage uplift against your actual threat model — and design each one against a structured specification before a line of detection logic is written. Specifications are reviewed with your SOC lead before development begins.

3
Weeks 3–5

Development, Testing & Tuning

We develop detection logic in your native SIEM language, test each rule against historical data and known-good baselines, tune for false positive rate, and validate detection performance before requesting production deployment approval from your team.

4
Week 6

Deployment, Handover & Knowledge Transfer

We deploy validated use cases to production, deliver analyst triage runbooks for each detection, brief your SOC team on the new coverage and provide documentation that allows your team to maintain and extend the detection library independently going forward.

What You Receive
ATT&CK coverage heatmap — before and after
Detection use case library with full specifications
Analyst triage runbook for each deployed detection
Alert quality report — false positive root cause analysis
Threat intelligence operationalisation report
Tuning change log with before/after fidelity metrics
Detection engineering backlog for ongoing use case development
Platform-native detection logic files for your records

Most clients continue with a quarterly detection engineering retainer to maintain coverage uplift as the threat landscape and their environment evolves.

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Who This Is For

Right for you if any of
these situations sound familiar

Detection engineering is not only for large, mature SOCs. If your team is drowning in alerts, missing detections or consuming threat intelligence it can't act on, the problem is addressable — without replacing your platform.

Alert Fatigue

Your analysts have stopped trusting the alerts

When false positive rates climb above 40–50%, analysts begin applying informal triage heuristics — effectively ignoring alert categories they've learned to distrust. This is how real threats slip through. It is a detection engineering problem, not an analyst performance problem, and it is fixable.

Coverage Uncertainty

You don't know what you're not detecting

You receive regular threat intelligence briefings about adversary techniques targeting your sector. You don't know with confidence whether your current detection logic would catch any of them. That uncertainty — not the threat itself — is the most important thing to resolve first.

Platform Migration

You're moving SIEM platforms and need to rebuild properly

A SIEM migration is the highest-leverage point to rebuild your detection library correctly. Migrating existing rules directly translates your current coverage gaps into your new platform. We design detection use cases natively for your destination platform — not ported from a different language and architecture.

Post-Incident

You've had an incident and need to close the gap it exposed

A security incident is the most expensive way to discover a detection gap. After the immediate response, the priority is ensuring that the technique used — and the related techniques in the same ATT&CK tactic — are now detectable. We structure post-incident detection reviews to close the specific gap and the adjacent coverage gaps simultaneously.

Your Tools Are Capable. Let Us Configure Them To Prove It.

Find your detection gaps before
your adversaries find them for you.

A detection review engagement starts with a coverage assessment — typically completed within a week. We'll map your current ATT&CK coverage, identify your highest-priority gaps and give you a use case development plan your team can act on immediately.

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Engagements scoped to your platform stack and environment size. Typical first response within one business day.