IT CompTIA SecAI+

Course Code: ClaTech7399

Duration

5 Day(s)

Course Type

Public or Private

Max Delegates

12

Delivery Style

Classroom

No Upcoming Events

No upcoming Scheduled Courses. Contact us for information on booking a private course or join the waiting list for a public event.

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Securing Artificial Intelligence Systems Across the Enterprise

The CompTIA SecAI+ course provides a comprehensive foundation in securing artificial intelligence systems throughout their lifecycle. As organisations increasingly deploy AI-driven solutions, new threat vectors emerge across data, models, infrastructure, and governance. This course equips learners with the practical knowledge and skills needed to identify, assess, and mitigate AI-specific security risks while aligning AI deployments with enterprise security, risk, and compliance frameworks.

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Descriptions

Who is this course for

This course is intended for security professionals, IT and operations managers, governance and risk specialists, cloud and data practitioners, and technology leaders responsible for deploying, managing, or overseeing AI-enabled systems.

Purpose of the course

The purpose of this course is to prepare professionals to understand, manage, and secure AI systems responsibly. It bridges traditional cybersecurity principles with emerging AI technologies, enabling organisations to safely adopt AI while protecting data, intellectual property, and operational integrity.

You will learn how to


  • Understand core AI concepts, architectures, and enterprise use cases from a security perspective

  • Identify threats, vulnerabilities, and attack vectors unique to AI systems

  • Secure AI data pipelines, models, and deployment environments

  • Apply risk management, governance, and compliance controls to AI solutions

  • Integrate AI security practices into existing cybersecurity and operational frameworks

  • Respond effectively to AI-related security incidents and operational failures

Benefits for you as an individual

This course enhances your professional credibility by demonstrating your ability to secure AI systems in real-world environments. It builds future-ready skills at the intersection of cybersecurity and artificial intelligence, positioning you as a trusted advisor capable of guiding safe AI adoption, reducing risk, and supporting strategic technology decisions.

Benefits for your organisation

For organisations, this course supports safer and more confident AI adoption by reducing exposure to emerging AI-related threats. It helps build internal capability to manage AI risk, strengthen governance, and align AI initiatives with security, compliance, and operational objectives—protecting reputation, data assets, and business continuity.

Prerequisites

Recommended experience: 3–4 years in IT, inclusive of 2+ years hands-on cybersecurity; Security+, CySA+, PenTest+, or equivalent recommended.

It is also recommended that learners have, a basic understanding of cybersecurity concepts, familiarity with IT systems, cloud environments, or data management, general awareness of AI or machine learning concepts.

AI Concepts and Foundations for Security

  • Fundamental artificial intelligence and machine learning concepts
  • AI system components, workflows, and architectures
  • Common enterprise AI use cases and dependencies
  • Security considerations introduced by AI adoption

AI Threat Landscape and Attack Vectors

  • Adversarial machine learning techniques and threats
  • Data poisoning and training data manipulation
  • Model extraction, inversion, and theft attacks
  • AI supply chain and third-party risks

Securing AI Data and Models

  • Protecting training, validation, and inference data
  • Ensuring data integrity, confidentiality, and privacy
  • Model testing, validation, and hardening techniques
  • Secure model storage, versioning, and lifecycle management

AI Infrastructure and Deployment Security

  • Securing cloud-based and on-premises AI environments
  • Identity, authentication, and access control for AI systems
  • Monitoring, logging, and anomaly detection for AI workloads
  • API, platform, and integration security considerations

Governance, Risk, and Compliance for AI

  • AI risk assessment and threat modelling approaches
  • Ethical, responsible, and trustworthy AI principles
  • Regulatory, legal, and compliance considerations for AI
  • Policy development and organisational governance controls

Incident Response and Operational Resilience

  • Detecting AI-related security incidents
  • Responding to compromised data, models, or AI services
  • Business continuity and recovery planning for AI systems
  • Post-incident review and continuous improvement

Additional Exam Information

Duration: 60 minutes
Number of questions: Maximum of 60, multiple-choice and performance-based
Passing score: 600 (on a scale of 100–900)

Pre-Coursework

N/A

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