Certified AI Security Officer (CAISO)

The Certified AI Security Officer (CAISO) program, powered by Brit Certifications and Assessments (BCAA) and delivered by Cyberfox Train, is a specialized training designed for cybersecurity professionals, IT managers, and compliance officers who are navigating the evolving landscape of AI-driven threats and AI system security.

This 4-day hands-on training program empowers participants to understand AI system vulnerabilities, assess AI risk, implement defense strategies, and meet emerging compliance standards (including ISO/IEC 42001, NIST AI RMF, and EU AI Act).

Participants will also gain practical insights into adversarial attacks, AI model manipulation, and data poisoning, with real-world use cases across sectors like banking, healthcare, government, and critical infrastructure.

🎯 Course Objectives

Upon successful completion of CAISO, participants will be able to:

• Understand the unique risks and attack vectors associated with AI systems
• Analyze and defend against adversarial machine learning (AML) threats
• Apply AI governance and compliance frameworks (e.g., ISO 42001, NIST AI RMF)
• Secure the AI lifecycle from model development to deployment
• Conduct risk assessments and threat modeling for AI applications
• Develop AI security policies aligned with ethical and regulatory principles

👥 Target Audience

• Cybersecurity professionals and AI/ML engineers
• Chief Information Security Officers (CISOs) and Risk Managers
• Compliance and Governance officers
• DevSecOps engineers
• Public sector and critical infrastructure security professionals
• Anyone responsible for safeguarding AI-enabled systems

✅ Prerequisites

• Basic knowledge of cybersecurity and/or AI fundamentals
• Familiarity with machine learning and system architecture is helpful but not required
• Willingness to engage in technical labs, discussions, and case studies

✨ Key Features

• Instructor-led sessions with real-world labs
• Certificate issued by Brit Certifications and Assessments (BCAA), UK
• Based on ISO, NIST, and global AI security frameworks
• Hands-on exposure to adversarial attacks and AI red teaming
• Group activities and industry-specific case studies
• Exam & BCAA credential included

Artificial Intelligence Based Cyber Security Management System

Artificial Intelligence (AI) has revolutionized cybersecurity management, offering powerful tools and techniques to enhance threat detection, automate responses, and strengthen overall security postures. Here’s an overview of how AI is transforming cybersecurity management:

Enhanced Threat Detection and Prevention

AI-powered cybersecurity systems excel at analyzing vast amounts of data to identify patterns and anomalies that may indicate potential threats. These systems can:

• Monitor network traffic and user behavior in real-time
• Detect unusual activities or unauthorized access attempts
• Identify new and emerging threats, including zero-day exploits
• Predict potential future attacks based on historical data and trends

By leveraging machine learning algorithms, AI can continuously improve its threat detection capabilities, adapting to new attack vectors and evolving cyber threats.

Automated Incident Response

AI enables faster and more efficient incident response through automation:

• Rapid analysis of security alerts and prioritization of high-risk incidents – Automated containment measures, such as isolating compromised systems or blocking malicious IP addresses
• Streamlined incident management workflows
• AI-driven forensics to quickly identify the root cause of security breaches

This automation allows security teams to focus on more complex tasks and strategic decision-making, improving overall incident response times and effectiveness.

Vulnerability Management

AI enhances vulnerability management processes by:

• Conducting continuous scans to identify system weaknesses
• Prioritizing vulnerabilities based on their potential impact and exploitability
• Recommending appropriate patching and remediation strategies – Predicting potential vulnerabilities before they can be exploited

These capabilities enable organizations to proactively address security gaps and maintain a strong security posture.

Advanced User Authentication

AI improves user authentication and access management through:

• Behavioral biometrics analysis to detect anomalies in user patterns
• Risk-based authentication that adapts security measures based on context
• Continuous authentication throughout user sessions
• Enhanced fraud detection and prevention

These AI-driven authentication methods help balance security with user experience, reducing the risk of unauthorized access while minimizing friction for legitimate users.

Intelligent Threat Intelligence

AI powers more sophisticated threat intelligence capabilities:

• Analyzing and correlating data from multiple sources to provide actionable insights
• Identifying emerging threats and attack trends
• Generating real-time threat alerts and recommendations
• Automating the process of updating threat databases and security rules

Agenda:

Day 1: Introduction to AI Security

Morning Session
– Introduction to AI and its role in cybersecurity
– Overview of AI security challenges and risks
– Types of AI systems and their vulnerabilities
– AI-specific threats and attack vectors

Afternoon Session
– Ethical considerations and responsible AI practices
– Regulatory landscape for AI security (e.g., EU Artificial Intelligence Act)

Day 2: Securing AI Models and Infrastructure

Morning Session
– Identifying vulnerabilities in AI models and datasets
– Adversarial attacks and defenses
– Secure data handling and privacy preservation in AI
– Model theft: risks, attack types, and protections

Afternoon Session
– Securing AI infrastructure and cloud deployments
– Secure coding practices for AI systems
– Authentication and access control for AI systems

Day 3: AI-Powered Security and Incident Response

Morning Session
– AI-powered threat detection and SIEM
– Developing and implementing AI-based threat detection systems
– AI-powered Endpoint Detection and Response (EDR)

Afternoon Session
– Monitoring AI systems for security breaches
– Detection and response to AI-specific attacks
– Forensics and investigation in AI security incidents

Day 4: AI Security Management and Best Practices

Morning Session
– Integrating AI security into Enterprise Risk Management
– NIST AI Risk Management Framework 1.0: Core Functions and Categories
– Secure AI Development Lifecycle
– Human-AI interaction: Ensuring safe and reliable outputs

Afternoon Session
– Best practices for AI security management
– Case studies: Analyzing real-world AI security incidents
– Developing an AI security action plan for organizations
– Wrap-up discussion

Exams

• The Training is followed by Subjective exam for three hours.
• You need to deliver a video post the exam.
• Submit an article to gain your certificate.