The cybersecurity AI-augmented SOC course for practitioners who need to win the alert-volume race.

• Lesson 1.1 — What AI does and doesn't do for security operationsFree preview28 min
• Lesson 1.2 — The AI-augmented SOC visionFree preview25 min
• Lesson 1.3 — Current AI security products30 min
• Lesson 1.4 — The skill shift: what changes for SOC analysts25 min
• Lesson 1.5 — The AI security operations maturity model28 min

• Lesson 2.1 — The cybersecurity incident response lifecycle45 min
• Lesson 2.2 — Detection logic and the anatomy of a security alert48 min
• Lesson 2.3 — SIEM fundamentals and log analysis52 min
• Lesson 2.4 — SOAR and automation in the SOC44 min
• Lesson 2.5 — Threat intelligence in practice46 min
• Lesson 2.6 — SOC metrics and performance measurement38 min

• Lesson 3.1 — MITRE ATT&CK structure and navigation40 min
• Lesson 3.2 — ATT&CK for detection engineering50 min
• Lesson 3.3 — ATT&CK for threat hunting45 min
• Lesson 3.4 — MITRE ATLAS: adversarial AI techniques48 min
• Lesson 3.5 — Mapping a breach to ATT&CK and ATLAS50 min

• Lesson 4.1 — The alert overload problem and why triage fails42 min
• Lesson 4.2 — Triage decision framework: NIST SP 800-61 and Funnel of Fidelity48 min
• Lesson 4.3 — Prompt engineering for alert triage54 min
• Lesson 4.4 — False-positive reduction with AI46 min
• Lesson 4.5 — Building and evaluating a triage prompt library50 min

• Lesson 5.1 — The forensic investigation lifecycle: NIST SP 800-8644 min
• Lesson 5.2 — Evidence collection with Velociraptor and KAPE52 min
• Lesson 5.3 — Memory forensics fundamentals with Volatility55 min
• Lesson 5.4 — Timeline reconstruction and AI correlation50 min
• Lesson 5.5 — AI hypothesis generation and evidence-gap analysis55 min
• Lesson 5.6 — Human-verification checkpoints and defensible documentation48 min

• Lesson 6.1 — Hunt fundamentals: hypothesis-driven methodology45 min
• Lesson 6.2 — Building hunt hypotheses from ATT&CK and threat intelligence48 min
• Lesson 6.3 — AI-assisted query generation for KQL and SPL52 min
• Lesson 6.4 — Behavioral analysis and anomaly detection with AI50 min
• Lesson 6.5 — Closing the loop: from hunt finding to detection rule44 min

• Lesson 7.1 — The AI threat landscape: MITRE ATLAS and OWASP LLM Top 1052 min
• Lesson 7.2 — Detecting prompt injection in production logs55 min
• Lesson 7.3 — Model abuse and unauthorized AI usage detection48 min
• Lesson 7.4 — AI agent misuse and autonomous action detection58 min
• Lesson 7.5 — Supply chain and training data attack detection50 min
• Lesson 7.6 — Detecting AI-enabled social engineering (deepfakes, AI phishing)56 min

• Lesson 8.1 — Playbook design principles for AI incidents48 min
• Lesson 8.2 — Prompt injection incident response playbook52 min
• Lesson 8.3 — AI agent misuse incident response playbook50 min
• Lesson 8.4 — Model abuse incident response playbook54 min
• Lesson 8.5 — Playbook automation and SOAR integration46 min

• Lesson 9.1 — The AI-augmented SOC integration framework46 min
• Lesson 9.2 — Tool selection and evaluation criteria44 min
• Lesson 9.3 — Phased deployment: from pilot to production48 min
• Lesson 9.4 — Measuring AI tool impact on SOC performance50 min
• Lesson 9.5 — Human-in-the-loop governance and rollback52 min

• Lesson 10.1 — The AI security tool evaluation framework48 min
• Lesson 10.2 — Telemetry fit and integration depth in cybersecurity AI tools44 min
• Lesson 10.3 — Testing AI cybersecurity tools against your data42 min
• Lesson 10.4 — Scoring, selecting, and documenting AI security tool decisions38 min

• Lesson 11.1 — Why AI usage policies fail and what makes them work46 min
• Lesson 11.2 — Acceptable use and data classification for AI interactions in cybersecurity48 min
• Lesson 11.3 — AI agent action limits and human-in-the-loop controls in cybersecurity52 min
• Lesson 11.4 — Audit, monitoring, and enforcement of AI usage in cybersecurity44 min
• Lesson 11.5 — Rollout, training, and AI policy lifecycle management in cybersecurity40 min

• Lesson 12.1 — SOC metrics foundations: MTTD, MTTR, dwell time, and false positive rate48 min
• Lesson 12.2 — Measuring AI tool contribution: baselines, deltas, and attribution46 min
• Lesson 12.3 — Governance and risk metrics for AI-augmented operations42 min
• Lesson 12.4 — Board-ready metrics narratives44 min

• Lesson 13.1 — Maturity assessment: SOC-CMM and the AI-augmented dimension50 min
• Lesson 13.2 — Designing the target state: capability architecture for AI-augmented operations48 min
• Lesson 13.3 — Building the transformation roadmap: phasing, dependencies, and risk52 min
• Lesson 13.4 — The people dimension: hiring, training, and change management46 min
• Lesson 13.5 — Writing the executive business case44 min

• Capstone Rubric — AI Security Operations Mastery15 min

The course is built on the augmentation model, not the replacement thesis. AI reduces the alert-volume tax on Tier 1 and Tier 2 analysts and accelerates investigation, but the course treats human judgment as mandatory in the loop. Every playbook module covers where HITL checkpoints must sit and why automating past them is a governance and liability problem.

AI classifiers trained on historical data inherit its class imbalance and can hallucinate benign context. Module 4 (AI-augmented alert triage) covers false-positive economics directly: measuring baseline FP rates, evaluating vendor FP claims, and designing feedback loops that improve classifier accuracy over time in your specific environment.

Module 10 (AI security tool evaluation) uses a six-dimension scorecard covering capability fit, data residency, integration depth, vendor dependency risk, total cost (including model consumption), and exit criteria. The course is deliberately vendor-pluralist: Microsoft Copilot for Security, Splunk AI, IBM QRadar, AWS Bedrock, and Lakera Guard are all covered without endorsement of any single vendor.

Module 9 and Module 10 work through the real cost model: token consumption at scale, per-seat licensing on top of existing SIEM spend, integration engineering hours, and the ongoing cost of model updates. The course builds a cost-benefit framework so you can present a defensible business case to your CISO or CFO.

Module 3 is a dedicated 4-hour module. It covers ATT&CK Enterprise, Cloud, ICS, and Mobile matrices plus ATLAS adversarial AI techniques (model inversion, training data poisoning, prompt injection, model evasion, supply chain compromise of ML systems). Module 7 applies that mapping to detection rule writing for AI-specific threats.

Module 11 (AI usage policies and controls) covers NYDFS Part 500 AI governance expectations, HIPAA risk analysis obligations for AI-assisted clinical security, and SR 11-7 model risk management for financial services SOCs. The module also addresses NIST AI RMF governance functions and how they map to SOC operating procedures.

SANS SEC555 covers SIEM tactical analytics; FOR578 covers cyber threat intelligence. This course presupposes that foundation and builds the AI-specific layer on top. The methodology synthesis matrix shows exactly what SEC504, SEC511, SEC555, and FOR578 each contribute versus what the AI-augmentation layer adds. This is the next step after SANS, not a replacement for it.

Approved capstones earn the AI Security Operations Mastery verifiable credential, signed with Ed25519 and embeddable on LinkedIn. The capstone is a 30 to 40 page AI-augmented SOC design document plus a 30-minute presentation reviewed by the founder. The credential is renewable through one continuing-practice exercise per year.

Yes. Fourteen-day full refund window from purchase. Email support@decipheru.com with your order number and we process the refund within 3 business days. After 14 days, refunds are evaluated case by case.

The required baseline is 1 or more years of SOC operations or equivalent CTF and blue-team experience, plus familiarity with at least one SIEM. If you are below that, the DecipherU SOC Analyst Fundamentals course covers the operational foundation. Enrolling in AI Security Operations Mastery without the baseline means the early modules will feel rushed and the capstone will be very difficult to complete.