About the Workshop This 8-day intensive program focuses on bridging AI concepts into each of the eight CISSP domains. Instead of traditional labs, participants will generate practical toolkits (policies, checklists, frameworks, etc.) that they can use immediately in their organizations. Target Audiences – CISSP-Certified Professionals...
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Course Modules & Topics (Focuses on the CISSP’s domains):
- Course Introduction
- Certificate Introduction & Exam Details.
- 1.1 AI-Driven Governance and Policy : How to update existing security policies with AI considerations, including ethical AI guidelines.
- 1.2 AI in Risk Assessment: Leveraging predictive analytics for threat forecasting and prioritization.
- 1.3 Compliance and Regulatory Requirements: Incorporating evolving AI regulations into standard compliance checklists (e.g., GDPR, ISO 27001).
- 2.1 Data Classification with AI: Automating classification processes using machine learning.
- 2.2 AI-Enabled Asset Discovery: Identifying unknown or untracked assets through anomaly detection.
- 2.3 Information Lifecycle Management: Managing data retention and destruction policies with AI-driven audits.
- 3.1 AI-Augmented System Design: Integrating machine learning models into secure-by-design principles.
- 3.2 Hardware and Firmware Considerations: Evaluating AI accelerators, edge devices, and their security implications.
- 3.3 Emerging Technologies: Quantum-resistant cryptography and how AI aids in designing future-proof architectures.
- 4.1 AI in Network Segmentation: Automating segmentation policies based on real-time traffic analytics.
- 4.2 Intelligent Threat Detection: Machine learning for anomaly detection, intrusion prevention, and traffic flow analysis.
- 4.3 Secure Protocols and Encryption: Assessing AI’s role in identifying vulnerabilities in encryption algorithms and protocols.
- 5.1 Biometric Authentication with AI: Facial recognition, voice authentication, and continuous monitoring for identity assurance.
- 5.2 Adaptive Access Control: Using AI to determine dynamic access privileges based on user behavior patterns.
- 5.3 Zero Trust Models: Leveraging AI to enforce just-in-time access decisions and micro-segmentation.
- 6.1 Automated Vulnerability Scanning: Enhancing traditional tools with AI to reduce false positives and streamline reporting.
- 6.2 Penetration Testing with AI: Scripted AI bots to discover complex attack paths and misconfigurations.
- 6.3 AI-Driven Compliance Testing: Crafting checklists to validate adherence to regulations and internal policies.
- 7.1 AI in Security Operations Centers (SOCs): Implementing ML-based event correlation, automated alert triaging, and real-time threat hunting.
- 7.2 Incident Response Automation: Leveraging AI to detect attacks faster and orchestrate response playbooks.
- 7.3 Behavior Analytics & Insider Threats: Using AI to profile normal behaviors and flag suspicious deviations.
- 8.1 AI-Enhanced Code Review: Tools and approaches for automatically identifying security flaws during development.
- 8.2 Secure DevOps Pipelines: Integrating AI-based vulnerability scanning into CI/CD workflows.
- 8.3 AI for Threat Modeling: Generating risk scenarios and countermeasures early in the software lifecycle.
Course Content
Course Introduction & AI Security Literacy
CISAIP Course Domains
CISAIP - Exam Review & Preparation
Course Evaluation & Certificate of Attendance
