Detection Engineering Program - Part 2 - Detection Engineering High Level Frameworks
Detection Engineering Program: High-Level Frameworks
As detection engineering has matured, practitioners have developed frameworks to make detection programs more structured, consistent, and measurable. One of the earliest influential models was Palantir’s Alerting and Detection Strategy (ADS) Framework, introduced nearly a decade ago and subsequently refined.
The Problem: Inconsistent Detection Practices
Historically, many organizations developed custom detections without a common framework. Different engineers and teams followed their own formats, processes, and standards, resulting in significant inconsistencies (e.g., write a query load it into the SIEM).
These inconsistencies affected two major areas:
Detection form — The information, logic, and documentation contained within each detection
Detection process — The lifecycle used to select, design, develop, test, deploy, and maintain detections
Without standardization, detections become difficult to understand, reproduce, measure, maintain, and improve. This limits an organization’s ability to assess and advance the maturity of its detection program.
Detection Form: What Should Every Detection Contain?
A well-structured detection should answer several essential questions:
What threat, behavior, or condition is the detection designed to identify?
What is the detection’s objective and expected security value?
How does the detection logic work?
Which data sources and fields does it require?
What priority or severity should be assigned when it triggers?
What known false positives and legitimate use cases exist?
What blind spots, assumptions, and technical limitations apply?
How was the detection tested and validated?
What should the SOC do when the detection generates an alert?
A detection is incomplete if it generates an alert but does not provide analysts with enough context to understand, investigate, and respond to it.
Detection Process: How Should Detections Be Managed?
A mature detection engineering process should address several fundamental questions:
Is the organization developing detections that align with its threats, risks, and mission?
Are detections designed and documented using a consistent standard?
How are detection requirements prioritized and approved?
How are detections developed, tested, and validated?
How are detections deployed and monitored in production?
How are performance, false positives, and coverage measured?
How are detections tuned, updated, and eventually retired?
The Relationship Between Process and Form
A strong detection engineering process produces consistent, high-quality detection content. Standardized detection forms then make that process easier to repeat, govern, measure, and improve.
A mature detection program requires both: a standardized detection lifecycle and a standardized structure for every detection produced.
Palantir’s Alerting and Detection Strategy Framework
Purpose: Palantir introduced the Alerting and Detection Strategy (ADS) Framework in 2017 to replace inconsistent, ad hoc alert development with a documented, repeatable, and peer-reviewed process.
Problems the Framework Addresses
Incomplete or outdated detection documentation
Excessive false positives and missed malicious activity
Unclear alert priorities and response procedures
Unvalidated detection logic and data assumptions
Production deployment without testing or peer review
Required Components of an ADS
Goal — Defines the behavior the detection is intended to identify.
Categorization — Maps the detection to relevant MITRE ATT&CK techniques.
Strategy Abstract — Summarizes the logic, telemetry, enrichment, and filtering.
Technical Context — Gives responders the knowledge needed to understand the alert.
Blind Spots and Assumptions — Documents conditions under which the detection may fail.
False Positives — Identifies legitimate activity that may trigger the detection.
Validation — Defines a repeatable method for generating and testing a true positive.
Priority — Establishes the alert’s severity and prioritization criteria.
Response — Provides steps for triaging, investigating, and escalating the alert.
Additional Resources — Links supporting technical and threat information.
Key Takeaway
Every detection should be documented, tested, peer-reviewed, centrally stored, version-controlled, and updated throughout its operational lifecycle. This improves alert quality, responder readiness, consistency, and detection-program maturity.
Evolving Beyond Palantir’s ADS Framework
An example of how detection engineering frameworks continue to evolve is DCG420’s expansion of Palantir’s original Alerting and Detection Strategy (ADS) Framework into a new Detection Analytic Template.
Why Move Beyond ADS?
Palantir’s 2017 ADS framework helped detection teams replace ad hoc rules and institutional knowledge with consistent documentation, technical context, validation, and response procedures.
DCG420 argues that modern detection programs now require more than descriptive documentation. Detections must be engineered for:
Measurable resilience
Behavioral coverage
Evasion resistance
Automated response
Machine-readable management
Programmatic measurement at scale
How the New Template Evolves ADS
Traditional ADS Approach | Updated Detection Analytic Approach |
Goal describes the detection’s purpose | Hypothesis defines a specific, testable statement about adversary behavior |
Technical Context explains effectiveness through narrative | Robustness scoring measures how difficult the detection and its data source are to evade |
Response provides general analyst instructions | Manual and SOAR playbooks separately define human and automated actions |
Validation confirms that the rule triggers | Evasion-aware testing evaluates multiple ways an attacker could perform the same behavior |
Detection documents are primarily human-readable | Structured fields and standardized IDs support automation, queries, metrics, and sharing |
Two Foundational Concepts
Capability Abstraction — Detect the adversary’s underlying operation rather than a specific tool or filename. For example, detect access to LSASS memory instead of only looking for mimikatz.exe.
Summiting the Pyramid — Score detection robustness based on how difficult the detected behavior is for an adversary to change and how resistant the supporting telemetry is to evasion.
Operational Benefits
The new template is intended to help organizations:
Create detections based on behaviors rather than brittle indicators
Compare detection strength using standardized robustness scores
Connect alerts directly to manual and automated response actions
Test detections against alternative attacker implementations
Query and measure an entire detection library programmatically
Share structured detection knowledge through platforms such as MISP
Key Takeaway
ADS established the importance of documenting detections; the new template extends that foundation by treating detections as measurable, testable, automatable, and scalable engineering products.
Source: Evolving Detection: Why We’re Moving Beyond ADS to a New Engineering Template
Snowflake’s Detection Development Lifecycle
Snowflake applies software-development principles to detection engineering through a repeatable process for creating, deploying, and maintaining high-quality threat detections. The lifecycle is divided into detection creation and detection maintenance.
Six Lifecycle Phases
Requirements Gathering — Centralize detection requests and prioritize them using organizational risk, threat intelligence, vulnerabilities, threat models, and available telemetry.
Design — Convert the detection goal into a documented strategy. Snowflake uses Palantir’s ADS framework and involves Incident Response early to review the design and prepare triage playbooks.
Development — Translate the approved design into standardized detection code enriched with metadata such as MITRE ATT&CK mappings, platform, data source, and audit requirements.
Testing and Deployment — Test detections against historical and real-time data for accuracy, precision, alert volume, and false positives. Conduct peer review and manage the code through version control before production deployment.
Monitoring — Measure production performance, review assumptions and gaps, tune false positives, track improvement requests, and temporarily or permanently decommission ineffective detections when appropriate.
Continuous Testing — Regularly validate detections through Red Team and Purple Team exercises to confirm that they still detect their intended threats as technologies and attacker behaviors change.
Supporting Practices
Detection as Code — Standardized templates, version control, peer review, and documented changes
Cross-Team Collaboration — Input from Threat Intelligence, Incident Response, Red Team, Security, Audit, and Compliance
Detection Improvement Requests — A formal process for correcting defects, improving enrichment, tuning noise, or rebuilding logic
Regular Reviews — Periodic evaluation of each detection’s goal, relevance, assumptions, scope, and technical logic
Controlled Decommissioning — Documented decisions to disable noisy, obsolete, or ineffective detections
Program Benefits
Higher-quality and more reliable detections
Better documentation and analyst readiness
Reduced false positives and alert fatigue
Measurable detection coverage and performance
Repeatable processes that support team growth
Continuous improvement as threats and systems evolve
Key Takeaway
A detection is not finished when it is deployed. It is a continuously managed security capability that must be monitored, tested, improved, and retired when it no longer provides sufficient value.
Source: Detection Development Lifecycle: Snowflake’s implementation of the Detection Development Lifecycle
Common Patterns Across Detection Engineering Frameworks
Industry frameworks such as Palantir ADS, DCG420’s Detection Analytic Template, and Snowflake’s Detection Development Lifecycle use different structures and terminology, but they share several foundational principles.
Five Common Framework Themes
Threat-Informed Requirements and Design
Define a clear detection goal or hypothesis based on relevant threats, organizational risks, attack behaviors, coverage gaps, and available telemetry.
Standardized Development and Documentation
Use consistent templates, metadata, technical logic, ownership, MITRE ATT&CK mappings, assumptions, blind spots, and known false positives.
Testing, Validation, and Peer Review
Test detections against historical data, representative attacks, alternative implementations, and expected benign activity before controlled deployment.
Actionable Alerting and Response
Ensure each detection has an appropriate priority and provides sufficient context, investigation guidance, and manual or automated response procedures.
Measurement and Continuous Improvement
Monitor detection quality, alert volume, false positives, coverage, resilience, and operational value; then tune, retest, update, or retire detections as necessary.
Applying the Framework
An organization does not need to reproduce any industry framework exactly. It should adapt the model to its technologies, risks, staffing, mission, and operational maturity.
However, the organization’s framework should address these common areas to ensure that its detection engineering process is:
Consistent and repeatable
Effective and operationally actionable
Measurable and auditable
Scalable across teams and technologies
Continuously improved over time
Key Takeaway
The framework’s name and format are less important than establishing a controlled, measurable process that consistently produces effective detections and demonstrates how detection engineering contributes to the organization’s overall cybersecurity posture.
Section Review: High-Level Detection Engineering Frameworks
Frameworks establish consistency. They provide a repeatable structure for designing, developing, testing, deploying, and maintaining detections.
Palantir ADS standardized detection documentation. Its model captures the detection goal, technical context, ATT&CK mapping, blind spots, false positives, validation, priority, and response procedures.
DCG420 evolved ADS toward engineering. Its Detection Analytic Template adds testable hypotheses, robustness scoring, evasion-aware testing, structured playbooks, automation, and machine-readable management.
Snowflake defined an end-to-end lifecycle. Its six phases are requirements gathering, design, development, testing and deployment, monitoring, and continuous testing.
The frameworks share five common areas:
Threat-informed requirements and design
Standardized development and documentation
Testing, validation, and peer review
Actionable alerting and response
Measurement and continuous improvement
No framework must be copied exactly. Organizations should adapt their approach to their threats, risks, technologies, mission, resources, and maturity.
Detections are continuously managed capabilities. After deployment, they must be monitored, tuned, retested, updated, and retired when they no longer provide sufficient value.
Key Takeaway
An effective detection engineering framework creates a controlled, repeatable, and measurable process that consistently produces reliable and actionable detections.
High-Level Detection Engineering Frameworks Quiz
Question 1
What is the primary purpose of a detection engineering framework?
A. Eliminate the need for detection engineers
B. Establish a consistent and repeatable detection process
C. Require every organization to use identical detection rules
D. Replace the organization’s incident-response procedures
Question 2
Which framework component helps analysts understand legitimate activity that may trigger a detection?
A. False positives
B. Detection priority
C. Threat hypothesis
D. Version history
Question 3
How does DCG420’s Detection Analytic Template expand upon Palantir’s ADS framework?
A. It removes testing and documentation requirements
B. It focuses exclusively on detecting known malware
C. It adds testable hypotheses, robustness scoring, and structured response playbooks
D. It replaces behavioral detections with static indicators
Question 4
Which activity occurs after a detection has been deployed?
A. Stop measuring the detection because development is complete
B. Continuously monitor, test, tune, and review the detection
C. Remove its documentation and development history
D. Transfer all responsibility to the security-product vendor
Question 5
Does an organization need to implement Palantir ADS, DCG420, or Snowflake’s framework exactly as published?
A. Yes, because modifying a framework invalidates it
B. Yes, but only for organizations with a dedicated SOC
C. No, because a framework is unnecessary for smaller organizations
D. No, but its approach should address the frameworks’ common foundational areas
Answer Key
B — Frameworks create a standardized, repeatable, and measurable detection engineering process.
A — Documenting known false positives helps analysts distinguish legitimate activity from potential threats.
C — DCG420 expands ADS with testable hypotheses, robustness scoring, evasion-aware testing, and structured manual and automated playbooks.
B — Deployed detections require continuous monitoring, testing, tuning, improvement, and eventual retirement when appropriate.
D — Organizations can adapt a framework to their mission, risks, technologies, resources, and maturity while retaining its foundational principles.
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