AI for Cybersecurity Practitioners — Curriculum & Syllabus | CyberIntel AI Academy
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AI for Cybersecurity Practitioners

In-person (Chantilly) or live online Intermediate ·$1,600

Apply AI to threat analysis, AppSec, DevSecOps, compliance workflows, vulnerability review, and secure AI use.

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No payment now — we’ll email the next cohort dates and pricing for both formats.

// who it's for

Who this program is for

  • Security analysts, engineers, and SOC practitioners
  • AppSec and DevSecOps professionals
  • GRC, risk, and compliance practitioners adopting AI
// what you'll learn

What you will learn

  • Apply AI to threat analysis and vulnerability review
  • Use AI across AppSec and DevSecOps workflows
  • Support detection, response, and SOC operations with AI
  • Streamline compliance, risk, and governance tasks
  • Adopt enterprise AI securely and defend against AI-specific threats

An instructor-led program delivered in person in Chantilly or live online. Sessions are hands-on and scenario-driven, with practical exercises throughout so security practitioners apply each technique to realistic security work alongside their peers.

// syllabus

Curriculum

The full program syllabus — expand any chapter to see its lesson-by-lesson detail.

01AI Foundations for Security PractitionersWhere AI fits across security work, and how to use it without adding risk.
  • 1.1Why AI matters for cybersecurity
    The impact of AI on security work.
  • 1.2What LLMs can and cannot do for security
    Realistic strengths and limits.
  • 1.3The AI security tooling landscape
    Tools and categories to know.
  • 1.4Setting up AI tools securely
    Configure AI without new exposure.
  • 1.5Benefits and risks of AI in security work
    Weigh value against risk.
  • 1.6Data sensitivity and AI usage rules
    Decide what AI may touch.
  • 1.7Where AI fits across the security lifecycle
    Map AI to each phase.
  • 1.8Human oversight in security decisions
    Keep analysts in control.
  • 1.9Measuring AI value in a security team
    Track real operational gains.
  • 1.10Pitfalls, bias, and false confidence
    Avoid trusting AI blindly.
  • 1.11Building a responsible AI security mindset
    Adopt AI with discipline.
  • 1.12Team norms for AI in security operations
    Agree on safe team usage.
02Prompting for Security WorkPrompting patterns for triage, analysis, and security artifacts.
  • 2.1Prompting fundamentals for security
    Core prompt skills for analysts.
  • 2.2Describing systems and threats to AI
    Give the model useful context.
  • 2.3Prompts for analysis and triage
    Speed up investigation with AI.
  • 2.4Structured output for security tooling
    Produce tool-ready formats.
  • 2.5Role prompting as a security expert
    Frame the model as a specialist.
  • 2.6Few-shot prompting with real artifacts
    Match your formats with examples.
  • 2.7Grounding answers in your environment
    Anchor output to real context.
  • 2.8Reusable security prompt templates
    Standardize prompts for the team.
  • 2.9Reducing risky or misleading output
    Cut down on dangerous errors.
  • 2.10Iterating prompts for accuracy
    Refine for trustworthy results.
  • 2.11Avoiding sensitive data exposure
    Keep secrets out of prompts.
  • 2.12Building a shared security prompt library
    Organize prompts for reuse.
03AI for Threat Analysis and IntelligenceAccelerate triage, enrichment, and reporting across threat data.
  • 3.1Summarizing threat intelligence reports
    Distill long intel into key points.
  • 3.2Extracting IOCs and TTPs
    Pull indicators and techniques.
  • 3.3Mapping activity to MITRE ATT&CK
    Frame behavior in ATT&CK terms.
  • 3.4Enriching and correlating indicators
    Add context to raw indicators.
  • 3.5Profiling threat actors with AI
    Summarize actor behavior and intent.
  • 3.6Triaging alerts at scale
    Prioritize alerts efficiently.
  • 3.7Drafting threat briefings
    Communicate threats clearly.
  • 3.8Spotting patterns across data
    Find connections in large data sets.
  • 3.9Validating AI threat conclusions
    Verify before you act.
  • 3.10Avoiding analytic bias
    Guard against skewed analysis.
  • 3.11Limits of AI in attribution
    Where attribution stays hard.
  • 3.12A threat-analysis walkthrough
    Apply the techniques end to end.
04AI in Vulnerability ManagementPrioritize, explain, and report vulnerabilities with context.
  • 4.1Understanding vulnerabilities with AI
    Make sense of complex flaws.
  • 4.2Summarizing CVEs and advisories
    Quickly grasp new vulnerabilities.
  • 4.3Prioritizing by risk and context
    Rank fixes by real risk.
  • 4.4Mapping vulnerabilities to assets
    Tie findings to your environment.
  • 4.5Drafting remediation guidance
    Explain how to fix issues.
  • 4.6Explaining technical risk to stakeholders
    Translate risk for decision-makers.
  • 4.7Reducing false positives
    Filter out noise in findings.
  • 4.8Tracking and reporting status
    Keep remediation on track.
  • 4.9Patch and mitigation planning
    Plan responses to vulnerabilities.
  • 4.10Validating AI severity assessments
    Confirm AI risk ratings.
  • 4.11Limits and cautions in scanning
    Know where AI can mislead.
  • 4.12A vulnerability triage exercise
    Practice prioritization hands-on.
05AI for Application Security (AppSec)Review code, model threats, and reduce noise in AppSec findings.
  • 5.1AppSec fundamentals with AI support
    AppSec basics, accelerated by AI.
  • 5.2Reviewing code for security issues
    Spot weaknesses in source code.
  • 5.3Explaining vulnerabilities in code
    Understand why code is unsafe.
  • 5.4Common weaknesses (OWASP Top 10)
    Recognize frequent flaw classes.
  • 5.5Secure coding suggestions
    Get safer code recommendations.
  • 5.6Threat modeling with AI assistance
    Model threats more efficiently.
  • 5.7Reviewing dependencies and SBOMs
    Assess third-party risk.
  • 5.8Drafting secure design notes
    Document secure design decisions.
  • 5.9Analyzing SAST/DAST findings
    Interpret scanner output.
  • 5.10Reducing noise in scan results
    Focus on findings that matter.
  • 5.11Validating AI security advice
    Verify before applying fixes.
  • 5.12An AppSec review mini project
    Run a guided code review.
06AI in DevSecOps PipelinesBring AI into CI/CD security reviews while keeping humans in the loop.
  • 6.1Security in the CI/CD lifecycle
    Where security fits in pipelines.
  • 6.2Adding AI to pipeline reviews
    Use AI in build and deploy gates.
  • 6.3Reviewing IaC for misconfigurations
    Find infrastructure security gaps.
  • 6.4Secrets detection and handling
    Catch and manage exposed secrets.
  • 6.5Container and image security checks
    Assess container risk.
  • 6.6Policy-as-code with AI help
    Author and review security policy.
  • 6.7Automating security documentation
    Generate and maintain docs.
  • 6.8Gating and risk decisions
    Decide what blocks a release.
  • 6.9Interpreting pipeline security results
    Make sense of scan output.
  • 6.10Avoiding insecure automation
    Don't automate away safety.
  • 6.11Keeping humans in the loop
    Preserve oversight in pipelines.
  • 6.12A DevSecOps integration walkthrough
    Apply the techniques end to end.
07AI for Detection, Logging, and SOC WorkflowsSpeed up log analysis, detection tuning, and SOC investigations.
  • 7.1Understanding logs and telemetry
    Read and reason about log data.
  • 7.2Summarizing and querying logs
    Find signal in large log sets.
  • 7.3Writing and tuning detection rules
    Author and refine detections.
  • 7.4Reducing alert fatigue with AI
    Cut noise to focus analysts.
  • 7.5Correlating events across sources
    Connect related activity.
  • 7.6Drafting SIEM queries
    Build queries faster with AI.
  • 7.7Enriching alerts with context
    Add detail analysts need.
  • 7.8Prioritizing SOC investigations
    Triage what to investigate first.
  • 7.9Documenting detections
    Record detections clearly.
  • 7.10Validating AI-suggested rules
    Test detections before deploy.
  • 7.11Limits and risks in detection
    Know where AI can fail.
  • 7.12A detection engineering exercise
    Build and tune a detection.
08AI for Incident ResponseUse AI to scope, document, and learn from incidents faster.
  • 8.1The incident response lifecycle
    The phases of effective IR.
  • 8.2Triage and scoping with AI
    Quickly assess incident scope.
  • 8.3Building incident timelines
    Reconstruct what happened.
  • 8.4Summarizing evidence and findings
    Distill investigation data.
  • 8.5Drafting containment steps
    Plan containment actions.
  • 8.6Communicating during incidents
    Keep stakeholders informed.
  • 8.7Root-cause analysis support
    Investigate underlying causes.
  • 8.8Generating post-incident reports
    Produce clear IR reports.
  • 8.9Lessons-learned and improvements
    Turn incidents into fixes.
  • 8.10Tabletop exercise support
    Run and document exercises.
  • 8.11Validating AI IR guidance
    Verify AI recommendations.
  • 8.12An incident response walkthrough
    Apply the techniques end to end.
09AI for Compliance, Risk, and GovernanceStreamline framework mapping, audits, and policy work with AI.
  • 9.1Mapping controls to frameworks
    Align controls to standards.
  • 9.2Summarizing policies and standards
    Digest long compliance text.
  • 9.3Drafting and reviewing policies
    Author clear, compliant policy.
  • 9.4Evidence collection and audits
    Support audit preparation.
  • 9.5Risk assessment with AI support
    Assess and document risk.
  • 9.6Gap analysis against frameworks
    Find compliance gaps.
  • 9.7Vendor and third-party risk
    Evaluate external risk.
  • 9.8Explaining compliance to stakeholders
    Communicate obligations clearly.
  • 9.9Privacy and data protection
    Address GDPR and similar rules.
  • 9.10Validating AI compliance output
    Verify before relying on it.
  • 9.11Limits of AI in audits
    Where human judgment is required.
  • 9.12A compliance documentation exercise
    Draft real compliance artifacts.
10Secure and Responsible Enterprise AI AdoptionAdopt AI across the enterprise while defending against AI-specific threats.
  • 10.1Risks of enterprise AI adoption
    Understand organizational risk.
  • 10.2AI usage policies and guardrails
    Set safe usage boundaries.
  • 10.3Protecting sensitive data in AI tools
    Prevent data leakage.
  • 10.4Prompt injection and AI-specific threats
    Defend against new attack types.
  • 10.5Securing AI integrations and APIs
    Harden AI-connected systems.
  • 10.6Shadow AI and governance
    Manage unsanctioned AI use.
  • 10.7Evaluating AI vendors securely
    Assess vendor security posture.
  • 10.8Monitoring and auditing AI use
    Track how AI is used.
  • 10.9Training teams on safe AI use
    Build organization-wide awareness.
  • 10.10Responsible disclosure and ethics
    Handle findings responsibly.
  • 10.11Building an enterprise AI security policy
    Codify enterprise guardrails.
  • 10.12Capstone: a secure AI adoption plan
    Produce a plan for your org.