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GASAE
The GASAE certification trains security engineers and SOC analysts to design, implement, and manage AI‑driven automation, covering ML‑based threat detection, LLM‑powered playbooks, and AI‑enhanced SOAR workflows.
180
Minutes
82
Questions
70/100
Passing Score
$979
Exam Cost
Who Should Take This
It is intended for security engineers, SOC analysts, or automation architects with at least three years of hands‑on security operations experience. These professionals seek to deepen expertise in integrating machine learning and large language models into detection and response pipelines, and to lead AI‑augmented automation initiatives across their organization.
What's Covered
1
AI/ML Integration for Security Operations
2
Automated Threat Detection with ML
3
LLM-Powered Security Automation
4
Purple Team Automation
5
Security Orchestration with AI (SOAR + AI)
6
AI Model Security and Supply Chain
What's Included in AccelaStudy® AI
Adaptive Knowledge Graph
Practice Questions
Lesson Modules
Console Simulator Labs
Exam Tips & Strategy
20 Activity Formats
Course Outline
60 learning goals
1
AI/ML Integration for Security Operations
3 topics
ML foundations for security engineering
- Identify supervised, unsupervised, and semi-supervised learning approaches and describe how each applies to security use cases including classification, anomaly detection, and clustering of security events.
- Describe common ML model evaluation metrics including precision, recall, F1-score, AUC-ROC, and false positive rates and explain their significance for security detection system performance tuning.
- Implement feature engineering pipelines that extract security-relevant features from network flow data, endpoint telemetry, authentication logs, and DNS query patterns for ML model training.
- Analyze model performance degradation by evaluating concept drift indicators, data distribution shifts, and feedback loop effects to determine when security ML models require retraining or replacement.
Security data pipeline architecture
- Identify security data sources including SIEM outputs, EDR telemetry, cloud audit logs, network flow records, and vulnerability scan results that feed ML-powered detection and automation systems.
- Implement data ingestion and preprocessing pipelines that normalize, enrich, and label security telemetry for model training including timestamp alignment, entity resolution, and threat intelligence enrichment.
- Evaluate data quality and labeling strategies by assessing ground truth availability, label noise impact, class imbalance severity, and annotation consistency for security ML training datasets.
ML model deployment in security pipelines
- Implement model serving infrastructure for real-time security inference including containerized model endpoints, batch prediction pipelines, and edge deployment for network sensor analysis.
- Configure model monitoring and observability including prediction latency tracking, drift detection alerting, accuracy metric dashboards, and automated rollback triggers for production security models.
- Analyze deployment architecture trade-offs by comparing inference latency, throughput requirements, cost constraints, and availability needs to select appropriate model serving patterns for security workloads.
2
Automated Threat Detection with ML
4 topics
Network and endpoint anomaly detection
- Describe anomaly detection algorithms including isolation forests, autoencoders, DBSCAN, and one-class SVM and explain their suitability for detecting network intrusions, lateral movement, and data exfiltration patterns.
- Implement network traffic anomaly detection models that baseline normal communication patterns and flag deviations indicating C2 beaconing, DNS tunneling, and unusual data transfer volumes.
- Implement endpoint behavior analysis using process execution sequences, file system activity patterns, and registry modification tracking to detect fileless malware and living-off-the-land techniques.
- Evaluate anomaly detection model performance by analyzing detection rates, false positive volumes, alert fatigue impact, and tuning effectiveness to optimize the balance between sensitivity and operational burden.
User behavior analytics and insider threat
- Describe user behavior analytics architectures that combine authentication logs, application access patterns, data movement tracking, and temporal baselines to model normal user activity profiles.
- Implement user behavior risk scoring models that assign dynamic risk scores based on authentication anomalies, access pattern deviations, data handling changes, and peer group comparisons.
- Analyze insider threat detection model outcomes by investigating high-risk score triggers, correlating alerts with contextual factors, and differentiating true insider threats from benign behavioral changes.
Malware and phishing classification
- Identify ML-based malware classification approaches including static feature extraction, dynamic behavioral analysis, and hybrid methods that combine PE header analysis with sandbox execution traces.
- Implement NLP-based phishing detection that analyzes email headers, body content, URL patterns, and sender reputation using transformer models and embedding similarity for real-time classification.
- Evaluate malware classifier robustness by testing against adversarial sample evasion, concept drift from new malware families, and label noise from automated sandbox classification errors.
Threat intelligence enrichment with ML
- Describe how NLP models extract indicators of compromise, threat actor TTPs, and vulnerability references from unstructured threat intelligence reports, advisories, and dark web sources.
- Implement automated threat intelligence enrichment pipelines that correlate extracted IOCs with internal telemetry, assign confidence scores, and update detection rules based on ML-prioritized threat feeds.
- Evaluate threat intelligence enrichment quality by measuring IOC accuracy, false positive correlation rates, time-to-detection improvement, and actionability of ML-generated intelligence products.
3
LLM-Powered Security Automation
4 topics
LLM integration for SOC operations
- Identify LLM application patterns for security operations including natural language SIEM queries, alert summarization, incident report generation, and threat intelligence processing.
- Implement LLM-powered alert triage that classifies incoming alerts, extracts key indicators, correlates related events, and generates investigation summaries to accelerate SOC analyst workflows.
- Configure LLM-based threat intelligence summarization that processes CTI feeds, extracts IOCs, maps TTPs to MITRE ATT&CK, and generates actionable briefings for security operations teams.
- Analyze LLM-generated security outputs by evaluating factual accuracy, hallucination rates, context retention across multi-turn investigations, and reliability under adversarial prompt manipulation.
Automated playbook and response generation
- Describe how LLMs can generate incident response playbooks from historical incident data, convert natural language SOPs into automated workflows, and suggest remediation actions based on alert context.
- Implement LLM-assisted playbook generation that converts incident patterns into structured response workflows with conditional logic, enrichment steps, and human approval checkpoints.
- Evaluate automated playbook quality by testing response correctness, edge case handling, escalation trigger accuracy, and human-in-the-loop override effectiveness to validate LLM-generated workflows.
RAG and knowledge management for security
- Implement retrieval-augmented generation systems that index security documentation, past incident reports, and runbooks to provide contextually grounded answers for SOC analyst queries.
- Analyze RAG system effectiveness by measuring retrieval relevance, answer grounding accuracy, latency impact, and knowledge freshness to optimize security knowledge management automation.
LLM security guardrails and safety testing
- Identify LLM security risks for defensive deployments including prompt injection against security copilots, hallucinated IOCs, confidential data exposure in queries, and adversarial manipulation of automated responses.
- Implement safety guardrails for security LLM deployments including input sanitization, output validation, PII redaction, and confidence thresholds that prevent automated actions based on unreliable model outputs.
- Evaluate LLM security guardrail effectiveness by red-teaming security copilots, testing prompt injection resistance, measuring hallucination rates on security queries, and assessing data leakage controls.
4
Purple Team Automation
3 topics
AI-driven attack simulation and emulation
- Describe automated adversary emulation frameworks that use AI to select attack techniques, adapt execution paths, and simulate threat actor behavior based on MITRE ATT&CK procedures.
- Implement AI-powered breach and attack simulation scenarios that automatically execute attack chains, collect detection evidence, and correlate results with defensive coverage maps.
- Analyze simulation results to identify detection gaps, measure mean time to detect across attack stages, and prioritize defensive improvements based on coverage heatmap analysis.
Continuous security validation
- Identify continuous security validation approaches including automated control testing, detection rule regression testing, and ML-driven configuration compliance verification.
- Implement automated detection rule testing pipelines that generate synthetic attack traffic, validate alert firing, check enrichment accuracy, and report detection coverage regressions after environment changes.
- Evaluate continuous validation program effectiveness by measuring detection coverage trends, regression detection speed, remediation cycle times, and overall security posture improvement over time.
Detection engineering with ML assistance
- Implement ML-assisted detection rule generation that analyzes attack telemetry patterns to suggest Sigma rules, YARA signatures, and behavioral detection logic for emerging threat techniques.
- Analyze detection rule quality by evaluating true positive rates, false positive volumes, detection specificity, and coverage against the MITRE ATT&CK matrix to optimize the detection engineering lifecycle.
5
Security Orchestration with AI (SOAR + AI)
3 topics
AI-enhanced SOAR architecture
- Describe SOAR platform architectures and explain how AI integration enhances alert triage, automated enrichment, response orchestration, and case management through intelligent decision support.
- Implement AI-powered alert routing that classifies incoming alerts by type, severity, and required expertise and assigns them to appropriate analysts or automated response workflows based on ML predictions.
- Configure ML-driven false positive reduction by training classifiers on analyst disposition data to automatically suppress known benign alerts while preserving sensitivity for novel threats.
- Analyze SOAR automation effectiveness by measuring mean time to respond, analyst workload reduction, automation coverage rates, and incident resolution quality before and after AI integration.
Intelligent response workflow optimization
- Implement adaptive response workflows that dynamically adjust containment actions, enrichment depth, and escalation triggers based on ML-assessed incident severity and blast radius predictions.
- Apply reinforcement learning concepts to optimize SOAR playbook execution by modeling response actions as sequential decisions that maximize containment speed while minimizing business disruption.
- Evaluate response workflow optimization outcomes by comparing pre- and post-automation metrics for containment time, incident recurrence rates, and analyst satisfaction across different incident categories.
Human-AI collaboration in incident response
- Implement human-in-the-loop decision frameworks that define when AI-driven response actions require analyst approval, override criteria, and escalation triggers for high-impact containment decisions.
- Evaluate human-AI collaboration effectiveness by measuring analyst trust calibration, override accuracy, automation acceptance rates, and incident outcome quality across different confidence thresholds.
6
AI Model Security and Supply Chain
2 topics
Defensive model robustness and testing
- Identify adversarial attack vectors against defensive ML models including evasion attacks on malware classifiers, data poisoning of training pipelines, and model extraction through API abuse.
- Implement adversarial robustness testing for security ML models using adversarial example generation, perturbation analysis, and red team evaluation to identify weaknesses before production deployment.
- Apply model hardening techniques including adversarial training, input preprocessing defenses, output calibration, and ensemble methods to improve defensive model resilience against evasion attacks.
- Evaluate the trade-offs between model robustness and detection accuracy by analyzing how adversarial hardening affects false positive rates, detection coverage, and computational overhead.
AI supply chain and model governance
- Identify AI supply chain risks including compromised pre-trained models, backdoored model weights, malicious model card manipulation, and dependency vulnerabilities in ML framework libraries.
- Implement model provenance verification including cryptographic signing, model card validation, training data lineage tracking, and reproducibility checks for third-party models integrated into security pipelines.
- Configure ML pipeline security controls including access control for training data, model registry authentication, inference endpoint authorization, and audit logging for model lifecycle events.
- Analyze AI governance requirements by evaluating regulatory compliance obligations, model explainability needs, bias detection requirements, and accountability frameworks for AI-driven security decisions.
Scope
Included Topics
- All domains covered by the GIAC AI Security Automation Engineer certification (GASAE) aligned with SANS SEC598: AI/ML Integration for Security Operations, Automated Threat Detection with ML, LLM-Powered Security Automation, Purple Team Automation, Security Orchestration with AI (SOAR + AI), and AI Model Security and Supply Chain.
- AI/ML integration for security operations centers including ML model deployment in detection pipelines, feature engineering from security telemetry, model training on labeled threat data, and real-time inference for alert triage.
- Automated threat detection: anomaly detection models for network traffic, user behavior analytics with unsupervised learning, malware classification with deep learning, and phishing detection with NLP.
- LLM-powered security automation: AI-assisted incident investigation, natural language query interfaces for SIEM, automated report generation, threat intelligence summarization, and playbook generation from incident patterns.
- Purple team automation: AI-driven attack simulation, automated adversary emulation, ML-guided detection gap analysis, and continuous validation of security controls through autonomous testing.
- Security orchestration with AI: SOAR platform AI integration, intelligent alert routing and enrichment, automated response workflow optimization, and machine learning for false positive reduction.
- AI model security and supply chain: model provenance verification, adversarial robustness testing for defensive models, ML pipeline security, and AI-specific supply chain risk management.
Not Covered
- Offensive AI attack techniques focused on compromising external targets that are covered by the GOAA certification rather than defensive AI automation.
- Advanced data science and statistical theory that exceeds the practical security automation scope of SEC598.
- General SOAR platform administration and playbook authoring without AI/ML integration components.
- Traditional SIEM rule writing and log parsing without machine learning enhancement that is covered by other GIAC certifications.
- Full-stack MLOps and model serving infrastructure outside the context of security operations integration.
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