AI SECURITY PRACTITIONER
We give you the frameworks, the foresight, and the authority to lead in AI security.
- 3 Days Intensive Training
- Industry aligned
Course Authored By:
Dhananjoy Chowdhury
PRICE:
£1499
$3,000
- Secure Payment via Stripe
- 7-Day Money-Back Guarantee
Next Session Starts:
8-10 April 2026
Application Deadline:
1st April 2026
Program Duration
3 Days Intensive training
Course' Mode Of Delivery
Instructor Led (Onsite or Virtual)
Hands-on Labs (Online)
Apply what you learn with exercises and labs
JUMP TO:
"An intensive hands-on programme that equips IT and cybersecurity professionals to understand AI technologies, identify AI-driven threats, secure LLM and agentic applications, and implement technical and risk controls. Participants gain practical lab experience, governance awareness, and the skills needed to protect organizations from emerging AI-powered cyber risks."
COURSE OVERVIEW
Stop feeling overwhelmed by the AI revolution. Become the proactive, trusted, and essential architect of your organization's AI defense.
An intensive hands-on programme that equips IT and cybersecurity professionals to understand AI technologies, identify AI-driven threats, secure LLM and agentic applications, and implement technical and risk controls. Participants gain practical lab experience, governance awareness, and the skills needed to protect organizations from emerging AI-powered cyber risks.
Join this 3-days course to explore AI and cybersecurity’s relationship. This course covers AI implementations, threats, attacks, and necessary controls for secure systems.
Gain insights into AI’s complexities in cybersecurity, learn real-life industry use cases, and discover techniques to protect your organization against emerging AI-powered threats.
Career Value Add
Foundational Knowledge: Gain an understanding of both AI technologies and cybersecurity fundamentals
Read More
Insight into AI Threats: Learn about the vulnerabilities associated with AI systems, how they are exploited.
Defensive Strategies: Acquire practical skills in protecting AI systems.
Strategic Insight: Equip yourself with the knowledge to think strategically about AI security in a business context.
Professional Growth: Earn a certificate upon completion, validating your expertise in AI Security Fundamentals.
What You Will Learn
- Overview of Artificial Intelligence including Agentic AI
- Practical applications of AI
- AI in Cybersecurity
- AI as a threat
- AI Frameworks
- Leveraging AI for continuous monitoring
- Encryption, Data Protection and AI
- AI Risk and Compliance Management
- Roles and Responsibilities for AI and cybersecurity
Business Takeaways
- Overview of Artificial Intelligence
- Practical Use cases of AI
- AI as a threat and a control
- Simulate AI driven attacks
- Securing AI systems
- Leveraging AI for technical controls
- Managing cybersecurity risks related to AI systems
- Assessing cybersecurity risks of AI systems
- Governing use of AI systems
What You Will Get
- 30+ Video Lessons
- Completion Certificate from London School Of Cybersecurity
- Digital Certification Badge from The British Computer School (BCS, The Chartered Institute for IT)
- 30 Days Access To Hands-on Labs
- Digital & Physical Materials
Course Curriculum
Day 1
Introduction to the ‘AI Explosion’
- Overview of Artificial Intelligence
- What is AI?
- History and Evolution of AI
- Types of AI
- Agentic AI and AI Agents
- Overview of Model Context Protocol (MCP) Overview
- Applications of AI in Different Industries
- AI in Cybersecurity
Threat Detection: AI systems identifying anomalies and potential breaches
Incident Response: Automated systems responding to cyber threats
Predictive Analysis: Using AI to forecast potential security incidents
- AI as a Threat
AI-Powered Attacks: Hackers leveraging AI to create sophisticated attacks
Adversarial AI: Manipulating AI systems to alter data and make wrong decisions
Deepfakes and Phishing: AI-generated content used for deceptive practices
- Hands-On Lab
- Create a AI Generated phishing campaign
- Create AI powered attack
Day 2
Technical Mitigation Controls
- Threats to AI Systems
- Robust AI Frameworks
- Implementing secure AI development practices
- Regular audits and updates to AI models
- OWASP Top 10 for LLM Applications
- OWASP Top 10 for Agentic Applications 2026
- Agentic AI and MCP Security Controls
- LLM Application Security
- Input/output validation and sanitization
- Implementing guardrails and safety filters
- Rate limiting and token management
Output filtering and content moderation
- Advanced Threat Detection Systems
- Leveraging AI for continuous monitoring
Incorporating anomaly detection algorithms
- Encryption and Data Protection
- Ensuring data integrity and confidentiality
Using AI for enhanced encryption techniques
- Use Case Deep Dive and Hands On Lab
- Testing AI security
- Simulating attacks on AI systems
- Prompt injection demonstration
- Deepfake detection exercise
- Implementing defenses
- Secure MCP server deployment
Day 3
Risk Management Controls
- AI Risk Management
- Developing a comprehensive AI risk management strategy
- Policies and Standards
- Training staff on AI Adoption
- Risks and secure adoption
- AI Governance Frameworks
- Risk Assessment
- Identifying potential AI-related risks
- Risk and Control Self-Assessments Use Cases
Third-Party AI Risk Management
AI Vendor Due Diligence
- Compliance and Regulatory Considerations
Available industry standards and regulations
Implementing AI-specific organization policies
- Roles & Responsibilities
Evolving roles and responsibilities in managing AI risks
Potential new roles to manage AI adoption and risks
- Use Case Deep-dive
Conduct sample risk assessment
- AI incident response tabletop exercise
- Using AI to create engaging Security Awareness materials
Key Takeways
- Understand core AI concepts, types, and real-world applications across industries.
- Learn how AI supports cybersecurity operations such as detection and response.
- Recognise AI-powered threats including deepfakes, automated phishing, and adversarial manipulation.
- Experience simulated AI-driven attack scenarios to build practical awareness.
- Apply security controls for AI and LLM applications such as input/output validation and guardrails.
- Implement monitoring, anomaly detection, and rate/token management techniques.
- Understand encryption and data protection considerations in AI environments.
- Gain hands-on experience testing vulnerabilities and implementing defensive measures.
- Develop a structured AI risk management and governance approach.
- Conduct AI risk and control assessments, including third-party and vendor due diligence.
- Align AI initiatives with policies, standards, and regulatory expectations.
- Define roles and responsibilities for secure and responsible AI adoption.
Meet Your Authors
Dhananjoy Chowdhury
Founder of Sinevis, Course Author And Lead Trainer
Seasoned cybersecurity leader and Principal Trainer at the London School of Cyber Security. With over 20 years of global experience across public and private sectors, specialising in advanced security solutions, automation, and risk management. A certified expert, STEM Ambassador, and Founder of Sinevis.
Priyanka Chatterjee
CEO, London School of Cyber Security And Principal Trainer
Founding partner, Women in Cyber Security Middle East. With 20+ years of global experience. Leads initiatives to close the cybersecurity talent gap, mentors professionals worldwide, and an award-winning cybersecurity educator and community leader.
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What You Should Know About The Course
IT / Cybersecurity Professionals
Computer (Laptop/Desktop) with Internet Access
Participants are expected to have knowledge of systems and cybersecurity in an organization.
Basic cybersecurity understanding is helpful but not mandatory.
AI foundations, real-world use cases, and AI-driven threat awareness such as deepfakes and automated phishing.
Technical mitigation controls, LLM guardrails, monitoring, anomaly detection, and vulnerability testing.
AI risk management, governance, compliance alignment, and defining secure AI roles and responsibilities.
Ability to secure AI systems, assess AI risks, and implement governance and compliance measures.
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