DecipherU · Course
AI Security Operations Mastery
For the SOC analyst, threat hunter, or cybersecurity operations leader watching AI-augmented detection rewrite the playbook. This 14-module course is for the practitioners who run the augmented SOC, not the ones whose alerts AI handles instead. Built on the SANS rigor (SEC504, SEC511, SEC555, FOR578) plus MITRE ATT&CK, MITRE ATLAS, NIST AI RMF, and the AI-augmented SOC patterns from Microsoft, Google, and Anthropic. 55-70 hours of study plus a 30-40 page capstone with rubric.
What completing this course actually does for your comp
BLS OES May 2024 + Lightcast 2024 AI premiumTarget role after completion
AI Threat Detection Engineer
Base comp band: $128K–$155K (BLS median ± Enterprise tier).
With AI fluency (this course)
$158K–$171K
+17% to +27% lift on AI-fluent postings (Lightcast 2024).
Time investment
~70 hours
Self-paced. Most learners ship the capstone within 8–12 weeks of focused study.
What this replaces
Pricing posture
Standalone: $597. Bundled inside Operator ($129/mo) and Frontier ($299/mo), pays for itself if you would buy 3+ standalone courses.
Course author
Julian Calvo, Ed.D., M.S.
Founder of DecipherU. Education researcher, software engineer, and applied AI specialist at the cybersecurity-AI convergence.
After the course, you can operate an AI-augmented SOC end-to-end: triage at scale, detect AI-specific threats, run hypothesis-driven hunts, write production playbooks, evaluate tools, and lead the integration program.
Triage materially more alerts per hour with AI assistance, measured
Build AI-augmented triage workflows with documented before-and-after throughput. The course requires a measured result on your own data, not a vendor claim.
Map any breach to ATT&CK and ATLAS techniques
Cover ATT&CK Enterprise, Cloud, ICS, and Mobile matrices plus ATLAS AI-specific attack techniques. Run the mapping exercise on a real or simulated campaign.
Run AI-augmented threat hunts with hypothesis discipline
Hypothesis-driven hunting with AI query generation, anomaly detection, and behavioral analysis. Produce a complete hunt report with methodology documentation.
Detect AI-specific threats in production logs
Write detection rules for prompt injection, AI agent misuse, model abuse, AI-generated phishing, deepfake-based fraud, and unauthorized AI usage. Rules deploy to your actual SIEM.
Write production AI SecOps playbooks with HITL checkpoints
Build at least 3 production-ready playbooks (prompt injection incident, model exfiltration, deepfake fraud) with full RACI, evidence preservation steps, and mandatory human-in-the-loop decision gates.
Evaluate and select AI security tools with a six-dimension scorecard
Score any AI security product on capability fit, data residency, integration depth, vendor dependency risk, total cost, and exit criteria. Separate signal from marketing.
Govern enterprise AI usage with policy and controls
Draft an organizational AI usage policy, shadow AI detection plan, and audit trail design that satisfies NIST AI RMF governance functions and NYDFS / HIPAA / SR 11-7 expectations.
Lead a 90-day AI-augmented SOC integration with measured outcomes
Build a transformation roadmap with phased milestones, risk register, tool selection criteria, team training plan, and a metrics framework covering both traditional SOC KPIs and AI-augmentation gains.
Fourteen modules cover the full AI-augmented SOC operating cycle: threats, fundamentals, ATT&CK and ATLAS mapping, triage, investigation, threat hunting, AI-specific threat detection, playbooks, tooling integration, tool evaluation, policy, metrics, transformation leadership, and a capstone. Module 14 is a complete AI-augmented SOC design document evaluated against the published rubric.
The course makes the synthesis explicit: each source framework contributes a defined layer, so practitioners know where any concept originates and how the AI-specific application layer extends it.
Your queue grows faster than your team. You know AI can help but every vendor demo promises magic and delivers a configuration project. The course teaches you to evaluate what AI actually does to throughput in your environment, measure it, and build the case for what to buy next.
You write detection logic and you want AI to generate and refine Sigma rules without hallucinating false-positive gaps. Module 7 covers AI-specific threat scenarios and the detection-engineering workflow, with exercises that produce rules you can test in your SIEM today.
Your CISO wants numbers, not enthusiasm. The capstone is a complete AI-augmented SOC design document: current state, target state, transformation roadmap, tool selection with justification, and a metrics framework with measured ROI. The course builds it section by section.
Required
Recommended
Reviews
Reviews from the first cohort arrive Q2 2026. Capstones in the top 10% may be anonymized into case studies with explicit permission from the practitioner.
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 evaluated against the published rubric. The credential is renewable through one continuing-practice exercise per year. Issued by DecipherU. It is a course-completion credential, not an accredited degree or a vendor-issued certification.
Yes. Seven-day full refund from purchase, while you have completed less than 10% of the course. Email support@decipheru.com with your order number; refunds process within 3 business days. After 7 days or above 10% completion, refunds are case-by-case. A refund triggers a 90-day lockout on re-purchasing this course or subscribing to a tier that bundles it.
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.
This course is part of a packaged path
Each path bundles the curriculum sequence, the compensation delta it unlocks, and the recommended courses (this one is on the list). If you are not sure which path matches your starting point, the 2-minute AI Risk Score routes you to the right one.
Convergence
Defend production AI systems.
The highest-paying convergence persona. Prompt injection, agent abuse, supply chain, model integrity, AI red teaming.
$150K → $310K-$520K · open path →
Convergence
Become the AI-augmented SOC analyst.
The fastest convergence track for working SOC analysts. AI-assisted triage, detection authoring, threat intel, automation.
$87K → $135K-$180K · open path →
AI Security Engineering
The engineering counterpart. Build, evaluate, and secure AI systems at the code and infrastructure layer. Bundled with this course in the AI Security Pack at $797.
$597
AI Engineering Mastery
Build the technical depth to evaluate AI tools credibly. RAG, fine-tuning, agent architectures, and evaluation frameworks.
$597
AI Governance and Risk
The compliance and risk angle. NIST AI RMF, EU AI Act, NYDFS, HIPAA, and SR 11-7 governance and controls for AI systems in regulated environments.
$497
Course content is for educational purposes only and does not constitute professional advice. All claims are supported by cited peer-reviewed academic research. DecipherU does not teach or reproduce any proprietary sales methodology. Verify all referenced sources independently.