Description

A focused course, tailored for you

Building a Cybersecurity Practice for AI-Augmented Threats (Deepfakes + Prompt Injection + Model Theft + Supply Chain)

Build the cybersecurity practice that handles AI-augmented threats in 12 weeks. Deepfake defence + prompt injection + model theft + AI supply-chain security + executive engagement.

AI-augmented threats reshaped enterprise cybersecurity in 2025-2026. Deepfake CEO fraud, prompt injection attacks on production LLMs, model theft from public-facing AI, and AI-supply-chain compromises are now standard threat-model items. Cybersecurity managers who can build the practice that handles these win the next round of consulting engagements.

$199 one-time

Tailored to your situation. Access within 24 hours. 30-day money-back.

Includes a hand-built implementation playbook delivered alongside course access, generated for your specific situation.

Why this course

The threat landscape changed materially over the past 18 months. Deepfake CEO fraud cost firms hundreds of millions in confirmed incidents in 2024-2025. Prompt injection attacks compromised production LLM systems at major banks and SaaS firms. Model theft through query-based extraction emerged as a documented attack pattern. AI supply chain compromises (poisoned training data, malicious model files on HuggingFace, compromised foundation-model API responses) became real incidents.

Cybersecurity practices that still focus only on traditional perimeter, endpoint, and identity miss what executives are now actually worried about. Practices that ship an AI-threat-aware engagement pack win the work.

This course teaches the 12-week build of an AI-augmented threats cybersecurity practice: deepfake defence (detection + verification + incident response), prompt injection defence (input filtering + output validation + isolation), model theft defence (rate limiting + watermarking + access control), AI supply chain security (model provenance + dependency scanning), and the executive engagement model. Twelve modules with deliverables. Plus a hand-built implementation playbook for your specific client engagement profile.

What you walk away with

A documented AI-augmented threat model.
A deepfake defence and incident response playbook.
A prompt injection defence architecture.
A model theft defence (rate limiting + watermarking).
An AI supply chain security framework.
An executive engagement model for board AI security briefings.
A 12-week build plan.

The 12 modules

Module 1. AI-augmented threat landscape 2026

Detailed walkthrough of confirmed incidents 2024-2026: deepfake CEO fraud cases (Arup Singapore $25M, Ferrari attempt, Hong Kong bank $25M), prompt injection at production LLMs (Bing Chat early attacks, Air Canada chatbot legal, Chevrolet dealer chatbot), model theft documented research, AI supply chain compromises (PoisonGPT, HuggingFace malware uploads), and the emerging deepfake-voice scam category. The threat-model items executives are now asking about.

Module 2. AI-augmented threat model framework

Build the AI-augmented threat model framework: STRIDE for AI (Spoofing via deepfake, Tampering via prompt injection, Repudiation via model output disputes, Information Disclosure via training-data extraction, DoS via resource exhaustion, Elevation of Privilege via jailbreak), MITRE ATLAS framework integration, OWASP LLM Top 10 alignment, and the client-engagement adaptation. The threat model that supports client conversations.

Module 3. Deepfake defence architecture

Build the deepfake defence architecture: detection technology (Reality Defender, Truepic, Hive AI, in-house ensemble), verification protocols for high-stakes communications (out-of-band confirmation, pre-shared secrets, video-call authentication), incident-response playbook (who calls whom in the first 60 minutes), and the executive awareness training. Three deepfake defence patterns from peer engagements.

Module 4. Prompt injection defence

Build the prompt injection defence architecture: input filtering (instruction extraction, jailbreak detection, indirect injection from web content), output validation (data leakage scan, toxicity scan, format validation), system-prompt isolation, sandboxed tool execution, content-security-policy-style restrictions, and the audit logging. Three prompt injection defence patterns with code examples.

Module 5. Model theft defence

Build the model theft defence: query-rate limiting (per-user, per-IP, per-tenant), watermarking (cryptographic and statistical), differential privacy for response generation, access-pattern monitoring (extraction-attempt detection), API authentication tightening, and the model-fingerprinting model. The defence that protects model IP.

Module 6. AI supply chain security

Build the AI supply chain security framework: model provenance tracking, signed model artefacts, training-data lineage, foundation-model vendor risk assessment, HuggingFace model scanning (Protect AI, JFrog, custom), AI-system bill of materials, and the third-party AI risk register. The supply chain framework aligned to Fed SR 22-6 and emerging EU AI Act provider obligations.

Module 7. AI-system DLP and data protection

Build the AI-system DLP: training-data classification, PII redaction at training, PII detection in production prompts and responses, RAG-source access control, conversation-data retention, and the GDPR/CCPA integration. The DLP that prevents inadvertent training-data leakage and accidental PII exposure in production AI responses.

Module 8. Foundation-model vendor risk

Build the foundation-model vendor risk framework: provider risk assessment (OpenAI, Anthropic, Google, AWS Bedrock, Azure OpenAI, Cohere, Mistral), data-handling clauses, model-update-notification clauses, breach-notification expectations, regional deployment options (EU Data Boundary, Sovereign Cloud variants), and the exit-strategy planning. The vendor risk framework that satisfies financial-services and regulated-industry clients.

Module 9. AI red-teaming methodology

Build the AI red-teaming methodology: scope definition (jailbreak, data extraction, model theft, deepfake susceptibility), automated red-teaming tools (PyRIT, Garak, in-house), human red-teaming protocols, finding-classification, remediation tracking, and the reporting cadence. Three AI red-team patterns with finding taxonomies.

Module 10. Incident response for AI incidents

Build the AI-specific incident response playbook: detection (what counts as an AI incident), classification (severity matrix), containment (model rollback, API circuit-breaker, customer notification), eradication (root cause for AI failures), recovery, lessons-learned, and the regulator notification (EU AI Act Article 73 serious incident reporting). The IR playbook for AI-specific incidents.

Module 11. Executive and board engagement

Build the executive and board engagement: CEO briefing format for AI threats, CISO partnership model, board-level AI security cadence, deepfake awareness training for executives (mandatory for CEO/CFO/Treasurer roles given fraud risk), and the quarterly reporting cadence. The engagement model that gets the practice funded and the work prioritised.

Module 12. Your 12-week build plan

Week-by-week plan with weekly deliverables. Weeks 1-2: AI-augmented threat landscape + threat model framework. Weeks 3-4: deepfake defence + prompt injection defence. Weeks 5-6: model theft defence + AI supply chain security. Weeks 7-8: AI-system DLP + foundation-model vendor risk. Weeks 9-10: AI red-teaming + incident response playbook. Weeks 11-12: executive engagement + practice launch. Deliverable: shippable AI-augmented threats cybersecurity practice.

How this addresses your situation

Specific modules that map to what you said you are dealing with.

Module 1 covers the threat landscape.

Module 2 covers the threat model framework.

Modules 3 to 6 produce deepfake, prompt injection, model theft, and supply chain defence.

Modules 7 to 10 cover DLP, vendor risk, red-teaming, and incident response.

Module 11 covers executive engagement.

Module 12 covers the 12-week build plan.

What you get with this course

The 12-module course delivered as text plus downloadable templates.
Templates for threat model framework, deepfake defence playbook, prompt injection defence architecture, model theft defence, AI supply chain security framework, AI-system DLP, foundation-model vendor risk, AI red-teaming methodology, AI incident response playbook, executive engagement playbook.
A hand-built implementation playbook generated for your specific client engagement profile.
Three worked examples of AI-threats cybersecurity practices at peer firms.
Scripted talking points for CEO and board engagement.

What you will have in hand by Day 1, Week 1, Month 1

Day 1: Threat model framework scaffold drafted.

Week 4: Deepfake + prompt injection defence built.

Week 8: DLP + vendor risk operational.

Week 12: Practice launched with first engagement underway.

Before and after

Before

Your cybersecurity practice still focuses on traditional perimeter, endpoint, and identity. Executives ask about deepfakes and prompt injection. The current engagement pack does not address either.

After

An AI-augmented threats cybersecurity practice is shippable. Deepfake defence, prompt injection defence, model theft defence, AI supply chain security, AI-system DLP, vendor risk framework, red-teaming methodology, AI incident response, executive engagement are all designed. Client engagements close because you address what executives now actually worry about.

What happens if you do not address this

Cybersecurity practices that do not handle AI-augmented threats lose engagements to firms that do. Specialist AI security firms (Lakera, HiddenLayer, Protect AI, Robust Intelligence) close the deals.

Who it is for

For cybersecurity managers, security architects, AI security engineers, and consulting practice leaders shipping AI-threat-aware engagements.

Who this is NOT for. Pure research roles. Firms not shipping cybersecurity engagements. Pure technology firms.

How it arrives

Text-based course via LMS, plus downloadable templates and the hand-built implementation playbook.

Time investment. Roughly 22 hours of reading and 80 to 150 hours of team effort across the 12-week build.

Why $199 is the right number

External AI security consultants charge $300K-$1.5M for practice builds. Specialist AI security firms (Lakera, HiddenLayer, Protect AI) charge $200K-$1M. Big4 cyber advisory engagement runs $500K-$2M. $199 buys the focused playbook plus the implementation document for your specific client engagement profile.

FAQ

Will this replace hiring an AI security specialist?

Partially. It teaches the practice build. You may still want specialist input for novel attack patterns.

What if my practice is broker-dealer or financial-services focused?

Module 8 covers financial-services vendor-risk specifically.

Does this cover quantum threats?

No, this is AI-augmented threats specifically. Quantum is a separate course.

What about CCM and CSA mapping?

Module 2 covers framework mapping including CSA AI Controls Matrix.

What is in the implementation playbook for me specifically?

Threat model framework tailored to your typical client; engagement-delivery patterns matched to your practice; a 12-week build plan.

30-day money-back guarantee. If after a week of working through the materials this is not what you needed, reply to the receipt email and a full refund is processed. No questions, no forms.

Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.