AI Security Operations Mastery: A 10-Week Cybersecurity Course

What this cybersecurity course is

AI Security Operations Mastery is a 10-week cybersecurity course for working SOC analysts, threat hunters, detection engineers, and security automation engineers who want to add production AI to their daily work. The curriculum sequences ten modules across the operational lifecycle of a modern AI-augmented SOC: foundations, prompt engineering for security operations, AI-powered alert triage, AI-augmented threat hunting, AI detection engineering, SOAR with LLMs, AI threat intelligence, AI security tool selection, AI in cloud security operations, and a capstone in which the learner designs and documents an AI-augmented SOC workflow for a hypothetical mid-size organization. Every module pairs hands-on practice with a primary-source reading set drawn from MITRE ATT&CK, MITRE D3FEND, NIST AI Risk Management Framework, CISA AI advisories, and the official documentation of the production AI security tools the course covers (Microsoft Security Copilot, CrowdStrike Charlotte AI, Splunk SOC Copilot, and the Anthropic Claude API as a generic LLM substrate). The course assumes a Security+ baseline plus practitioner SOC experience; it does not teach the security fundamentals. It teaches how to do the existing work better, faster, and at higher fidelity by treating AI as the working toolkit. Designed by Julian Calvo, Ed.D. in Learning Sciences (University of Miami), with the M.S. Applied AI specializing in Cybersecurity at Northeastern University in progress.

The course follows the operational lifecycle of an AI-augmented security operation rather than the chapter order of any single vendor product. Week 1 establishes capability mapping across the production AI security tools so the learner can evaluate any new tool that appears during the year. Weeks 2 through 9 walk the lifecycle: prompt engineering, alert triage, threat hunting, detection engineering, SOAR, threat intelligence, tool selection, cloud. Week 10 is a capstone that requires the learner to integrate the lifecycle into a single coherent workflow document. Pedagogically the design draws on Bandura's self-efficacy theory (1997) and Kolb's experiential learning cycle (1984): every module sequences a concept, a primary-source reading, a hands-on lab, and a written reflection note. The course is opinionated about evidence quality. Every claim is anchored to vendor official documentation, a public-domain government source (NIST, CISA, BLS), or a peer-reviewed paper. Vendor white papers without a primary-source backing are excluded. Exam dumps and proprietary training content are excluded.

What you will learn

• Map any new AI security operations tool against the four-layer capability matrix (data, model, action, governance) you build in week 1
• Write structured prompts that produce reviewable JSON for SOC alert triage, incident summarization, and analyst handoff notes
• Build a 30-case evaluation set against ground truth and use it to score AI outputs across the SOC pipeline
• Run AI-augmented threat hunts that translate hypotheses into KQL, SPL, or Lucene with named source citations
• Ship a Sigma-style detection rule with an LLM-generated enrichment field and measure its impact on false-positive rate
• Design SOAR playbooks with LLM decision points, schema validation, and reconstructable audit trails
• Produce analyst-grade threat intelligence with AI-assisted IOC extraction, correlation, and source ranking
• Evaluate AI security vendors against a primary-source rubric instead of marketing claims
• Run AI-augmented cloud security operations across AWS and Azure telemetry with anomaly narrative generation
• Defend a documented AI-augmented SOC workflow against three named failure modes: hallucination cascade, confirmation-bias amplification, and audit gap

10-week curriculum

1. Week 01 · 5.3h · 4 topics

   AI in Security Operations Foundations

   The shift from rule-based to AI-augmented security operations, capability mapping for production AI tooling (Microsoft Security Copilot, CrowdStrike Charlotte AI, Splunk SOC Copilot, Anthropic Claude API in security workflows), what AI does well and what it does not, and the AI-augmented analyst skill stack.

   Learning objectives and topics

   Learning objectives.

   • Describe the four functional layers of an AI-augmented SOC (data, model, action, governance) and map current production tooling to each
   • Compare the capabilities of Microsoft Security Copilot, CrowdStrike Charlotte AI, and Splunk SOC Copilot against a fixed set of analyst use cases
   • Identify the cognitive tasks where current LLMs reliably outperform a Tier 1 analyst, and the tasks where they reliably underperform
   • Build the AI-augmented analyst skill stack that Tier 1 through Tier 3 hires are expected to demonstrate by 2027

   Topics.

   • Why rule-based detection alone hit a ceiling
   • Capability mapping the production AI security tools
   • What AI does well in SOC work, and what it does not
   • The AI-augmented analyst skill stack

   Assessment: 8 questions · 320 minutes total

2. Week 02 · 6h · 4 topics

   Prompt Engineering for Security Operations

   Structured prompts for alert triage, log analysis, threat enrichment; prompt patterns including structured output, multi-step reasoning, and tool use; hallucination guardrails specific to security work; and how to evaluate prompt outputs against ground truth.

   Learning objectives and topics

   Learning objectives.

   • Write structured prompts that produce JSON-shaped, schema-validated output suitable for downstream automation
   • Apply chain-of-thought, tool use, and retrieval-augmented patterns to security-specific tasks
   • Build a small evaluation set (10 to 30 ground-truth cases) and score model outputs against it
   • Identify the three hallucination failure modes most common in security work and the prompt-level guardrails that mitigate each

   Topics.

   • Structured prompts for alert triage
   • Chain-of-thought, tool use, and retrieval patterns
   • Hallucination guardrails specific to security work
   • Building an evaluation set against ground truth

   Assessment: 9 questions · 360 minutes total

3. Week 03 · 6h · 4 topics

   AI-Powered Alert Triage

   LLM-driven alert summarization, auto-classification with priority scoring, cross-checking AI conclusions against raw evidence, and escalation criteria for AI-uncertain cases.

   Learning objectives and topics

   Learning objectives.

   • Design an LLM-driven summarization pipeline that compresses a 200-event timeline into a 4-sentence Tier-2-ready brief
   • Implement auto-classification with calibrated confidence scores and a documented threshold for human review
   • Cross-check AI conclusions against raw evidence using a structured spot-check protocol
   • Define escalation criteria for cases the AI flags as uncertain and the audit trail required for each

   Topics.

   • LLM-driven alert summarization
   • Auto-classification with priority scoring
   • Cross-checking AI conclusions against raw evidence
   • Escalation criteria for AI-uncertain cases

   Assessment: 9 questions · 360 minutes total

4. Week 04 · 6h · 4 topics

   AI-Augmented Threat Hunting

   Hypothesis-driven hunting at scale, LLM-assisted log query generation across KQL, SPL, and Lucene, pattern recognition across telemetry corpus, and IOC pivoting with LLM enrichment.

   Learning objectives and topics

   Learning objectives.

   • Generate hunt hypotheses from MITRE ATT&CK technique IDs and your SIEM's data inventory using a structured prompt
   • Translate hunt hypotheses into KQL, SPL, or Lucene queries with LLM assistance and validate the queries before running them
   • Apply LLM-assisted IOC pivoting to expand a single indicator into a complete cluster of related artifacts
   • Document the hunt outcome in a format that becomes new detection content or a runbook update

   Topics.

   • Hypothesis-driven hunting at scale
   • LLM-assisted query generation across KQL, SPL, and Lucene
   • Pattern recognition across telemetry corpus
   • IOC pivoting with LLM enrichment

   Assessment: 8 questions · 360 minutes total

5. Week 05 · 6h · 4 topics

   AI Detection Engineering

   ML-based detection vs rule-based detection trade-offs, building eval sets for detection rules, continuous evaluation in production, and false positive cost analysis.

   Learning objectives and topics

   Learning objectives.

   • Decide when to write a rule versus when to train a model for a given detection problem, using a documented decision matrix
   • Build an evaluation set for a new detection rule that measures precision, recall, and false-positive cost
   • Implement continuous evaluation in production so detection rules degrade visibly rather than silently
   • Apply false-positive cost analysis to a detection portfolio and decide which rules to retire, retune, or replace

   Topics.

   • ML-based detection versus rule-based detection
   • Building eval sets for detection rules
   • Continuous evaluation in production
   • False positive cost analysis

   Assessment: 9 questions · 360 minutes total

6. Week 06 · 6h · 4 topics

   AI Security Automation (SOAR with LLMs)

   Designing LLM-driven incident response playbooks, decision points and escalation criteria, hallucination guardrails in automation, and audit trails for AI-driven actions.

   Learning objectives and topics

   Learning objectives.

   • Design an LLM-driven SOAR playbook for a common alert class with explicit decision points and escalation criteria
   • Apply hallucination guardrails specific to automation contexts where the LLM's output triggers irreversible actions
   • Build the audit trail required for AI-driven actions to be defensible under SOC 2 and NIST CSF 2.0 review
   • Identify the three classes of action that should never be delegated to an LLM without human authorization

   Topics.

   • Designing LLM-driven incident response playbooks
   • Decision points and escalation criteria
   • Hallucination guardrails in automation
   • Audit trails for AI-driven actions

   Assessment: 9 questions · 360 minutes total

7. Week 07 · 6h · 4 topics

   AI Threat Intelligence

   LLM-assisted IOC extraction from vendor reports, multi-source CTI correlation, attribution analysis with AI tooling, and producing analyst-grade threat briefings.

   Learning objectives and topics

   Learning objectives.

   • Build an LLM pipeline that extracts IOCs from vendor PDF reports with citation back to the source paragraph
   • Correlate multi-source CTI to identify overlap, contradiction, and unique signal across vendor reports
   • Apply attribution analysis with AI tooling while staying within the Heuer ACH discipline of explicit alternatives
   • Produce an analyst-grade threat briefing in the format leadership reads in 5 minutes or less

   Topics.

   • LLM-assisted IOC extraction from vendor reports
   • Multi-source CTI correlation
   • Attribution analysis with AI tooling
   • Producing analyst-grade threat briefings

   Assessment: 8 questions · 360 minutes total

8. Week 08 · 6h · 4 topics

   AI Security Tool Selection and Evaluation

   Vendor capability assessment frameworks, evaluating AI security copilots (Microsoft, CrowdStrike, Splunk, others), building custom AI security tooling versus buying, and the cost economics of AI in security operations.

   Learning objectives and topics

   Learning objectives.

   • Apply a vendor capability assessment framework to evaluate any AI security copilot in three meetings or less
   • Compare Microsoft Security Copilot, CrowdStrike Charlotte AI, and Splunk SOC Copilot against a structured rubric
   • Decide build versus buy on AI security tooling using a TCO model that includes operations, integration, and lock-in
   • Calculate the cost economics of AI in security operations across token, license, integration, and analyst-hour line items

   Topics.

   • Vendor capability assessment frameworks
   • Evaluating AI security copilots head-to-head
   • Build versus buy on AI security tooling
   • Cost economics of AI in security operations

   Assessment: 9 questions · 360 minutes total

9. Week 09 · 6h · 4 topics

   AI in Cloud Security Operations

   AI-augmented IAM analysis, anomaly detection in cloud telemetry, LLM-driven misconfiguration detection, and multi-cloud AI security tooling.

   Learning objectives and topics

   Learning objectives.

   • Apply AI-augmented IAM analysis to detect over-privileged identities and access anomalies in AWS, Azure, and GCP
   • Build behavioral baselines for cloud telemetry and surface anomalies with LLM-generated narratives
   • Run LLM-driven misconfiguration detection against IaC (Terraform, CloudFormation) and live cloud state
   • Compare multi-cloud AI security tooling and design an evaluation strategy that survives the cloud-vendor differences

   Topics.

   • AI-augmented IAM analysis
   • Anomaly detection in cloud telemetry
   • LLM-driven misconfiguration detection
   • Multi-cloud AI security tooling

   Assessment: 8 questions · 360 minutes total

10. Week 10 · 8h · 3 topics

    Capstone and Certification

    Capstone project: design and document an AI-augmented SOC workflow for a hypothetical mid-size organization. Course wrap-up and certification of completion.

    Learning objectives and topics

    Learning objectives.

    • Design an end-to-end AI-augmented SOC workflow that integrates the patterns from weeks 1 through 9
    • Document the workflow with prompts, schemas, decision points, escalation criteria, and audit trails
    • Defend the workflow design against three named failure modes drawn from earlier modules
    • Earn the DecipherU AI Security Operations Mastery certificate of completion

    Topics.

    • Capstone scope and structure
    • Defending the workflow against failure modes
    • Course wrap-up and the next 12 months

    Assessment: 10 questions · 480 minutes total

Capstone

Design and document an AI-augmented SOC workflow for a hypothetical mid-size organization

The capstone is a 15 to 25 page workflow document that an AI-augmented SOC architect could read and implement. The hypothetical organization is a mid-size US firm with 5,000 employees, a Microsoft Defender plus Splunk stack, AWS plus Azure cloud presence, and a Tier 1 through Tier 3 SOC of 12 analysts. The deliverable has six required sections (architecture diagram, prompt library, decision-point catalog, audit trail spec, evaluation set spec, cost model) and is graded against three named failure modes: hallucination cascade, confirmation-bias amplification, and audit gap. A passing capstone scores 5 or higher across the three-failure-mode rubric and earns the DecipherU AI Security Operations Mastery certificate of completion.

Who it is for

• Working SOC analysts (Tier 1 through Tier 3) adding AI tooling to their daily triage, hunt, and response work
• Detection engineers integrating LLM-assisted query generation and behavioral ML into existing SIEM content
• Security automation engineers building SOAR playbooks that incorporate LLM decision points and audit trails
• Threat intelligence analysts producing analyst-grade briefings with AI-assisted IOC extraction and correlation
• Cloud security engineers running AI-augmented IAM analysis, anomaly detection, and misconfiguration review
• Security leaders evaluating AI security tooling vendors before procurement and rollout

Who it is not for

• Career changers who have not yet completed an entry-level cybersecurity foundation. Take the SOC Analyst Fundamentals course first.
• Practitioners looking for vendor exam prep. The course is tool-agnostic and does not credential against any single vendor exam.
• Engineers whose primary work is securing AI systems rather than using AI for cybersecurity operations. Take AI Security Engineering at /cyber-for-ai/courses/ai-security-engineering for that direction.
• Anyone hoping to skip the ground-truth evaluation work. The course requires building and maintaining an evaluation set across all 10 weeks.

Prerequisites

• CompTIA Security+ baseline knowledge (or equivalent practitioner experience)
• At least 6 to 12 months of hands-on SOC, detection engineering, or incident response work
• Working familiarity with at least one SIEM query language (KQL, SPL, Lucene, or EQL)
• Comfort reading API documentation and writing simple scripts (Python or PowerShell)
• Willingness to commit 50 to 65 hours of focused study across 10 weeks

What you get

• 56 hours of original cybersecurity curriculum across 10 weekly modules
• 10 hands-on labs that produce reviewable portfolio artifacts (eval set, prompt library, SOAR prototype, IOC pipeline, cloud anomaly pipeline, capstone document)
• Certificate of completion issued for learners who finish all 10 weekly assessments and submit a capstone that scores 5 or higher on the three-failure-mode rubric. The certificate is a digital credential with a verifiable URL listing the curriculum and the assessment outcomes.
• Lifetime access to course updates as MITRE ATT&CK, MITRE D3FEND, NIST AI RMF, and the production AI security tools evolve
• DecipherU community access (Defender tier and above) for peer review of the capstone and post-course Q&A

Author

Frequently asked questions

Who is this cybersecurity AI security operations course for?

Working SOC analysts (Tier 1 through Tier 3), detection engineers, security automation engineers, threat intelligence analysts, cloud security engineers, and security leaders evaluating AI security tools. The course assumes a Security+ baseline and at least 6 to 12 months of practitioner SOC, detection, or incident response experience. It teaches how to do existing security operations work better with AI, not security fundamentals.

What primary sources does the cybersecurity course cite?

MITRE ATT&CK, MITRE D3FEND, NIST AI Risk Management Framework (AI 100-1), CISA AI advisories, NIST SP 800-61 incident handling guidance, and the official documentation of the production AI security tools the course covers (Microsoft Security Copilot, CrowdStrike Charlotte AI, Splunk SOC Copilot, Anthropic Claude API). Vendor white papers without primary-source backing are excluded. Every claim is anchored to a public source.

How long does the cybersecurity course take to complete?

Roughly 50 to 65 hours of focused study across 10 weekly modules. Most learners complete it in 10 to 14 weeks at 5 to 7 hours per week. Self-paced. The capstone is the deliverable that earns the certificate of completion and produces the workflow document the learner can show a hiring panel or internal promotion review.

Will the course prepare me for a specific cybersecurity certification?

It is not a proctored exam prep course. The curriculum maps to the work products expected in AI-augmented SOC analyst, AI detection engineer, AI security automation engineer, and AI threat intelligence analyst roles, and to the Northeastern M.S. Applied AI specializing in Cybersecurity coursework. Pair it with vendor certifications (Microsoft SC-200, Splunk Core Certified User, CrowdStrike Falcon Administrator) for credentialed exams.

What is the capstone deliverable for the cybersecurity course?

A 15 to 25 page AI-augmented SOC workflow document for a hypothetical mid-size organization (5,000 employees, Microsoft Defender plus Splunk stack, AWS plus Azure cloud, 12-analyst SOC). The document has six required sections (architecture, prompts, decision points, audit trail, evaluation set, cost model) and is graded against three named failure modes: hallucination cascade, confirmation-bias amplification, and audit gap.

How does this course relate to AI Security Engineering?

AI Security Operations Mastery teaches practitioners who use AI to do cybersecurity operations work (the AI for Cybersecurity direction). AI Security Engineering at /cyber-for-ai/courses/ai-security-engineering teaches practitioners who secure AI systems themselves (the Cybersecurity for AI direction). Both reference the M.S. Applied AI specializing in Cybersecurity credential at Northeastern. Pick the one that matches your daily work.

Sister course

Securing AI systems instead of using AI for security operations?

AI Security Operations Mastery teaches the AI for Cybersecurity direction (use AI to do security operations work). The mirror course covers the Cybersecurity for AI direction (secure the AI systems themselves) across 12 weeks at $597.

Related cybersecurity content

• AI for Cybersecurity convergence area overview
• All AI for Cybersecurity career paths
• SOC Analyst Fundamentals (cybersecurity foundation course)
• Threat Intelligence Fundamentals cybersecurity course
• Cybersecurity for AI convergence area (the inverse direction)