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AI Internal Security Assessor (AIISA)

Length: 2 Days
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AI Internal Security Assessor (AIISA) Certification Program by Tonex

Digital Twin Technology & Model-Based Engineering Fundamentals Training by Tonex

This program focuses on securing internal AI systems and pipelines. Learn to conduct specialized threat and vulnerability assessments. Master techniques to identify risks like model leakage, shadow AI, and unauthorized API usage. Understand adversarial machine learning concepts applicable to Red/Blue teaming. Develop skills in creating risk dashboards for internal AI threats. This certification prepares internal auditors and Red Team members to proactively secure organizational AI assets from the inside. Enhance your expertise in AI-specific internal security assessments.

Audience: Internal cybersecurity auditors, Red Team members.

Learning Objectives: Upon completion, participants will be able to:

  • Understand the unique security threats within internal AI pipelines.
  • Identify vulnerabilities related to model integrity and data leakage.
  • Detect instances of shadow AI and unauthorized API access.
  • Apply concepts of Red/Blue teaming to assess ML model security.
  • Develop and interpret risk dashboards for internal AI threats.
  • Recommend mitigation strategies for identified internal AI vulnerabilities.

Program Modules:

Module 1: AI Security Fundamentals and Internal Threats

  • Introduction to AI security concepts.
  • Overview of the AI development lifecycle (MLOps) security.
  • Common internal threats to AI systems.
  • Understanding the internal AI attack surface.
  • Principles of secure AI architecture.
  • Role of the AI Internal Security Assessor.

Module 2: Assessing AI Pipeline Vulnerabilities

  • Analyzing data ingestion and preprocessing security.
  • Evaluating model training environment security.
  • Assessing model deployment vulnerabilities.
  • Reviewing API security for AI services.
  • Infrastructure security considerations for AI.
  • Secure configuration management for AI tools.

Module 3: Detecting Model Leakage and Data Exfiltration

  • Understanding model inversion and membership inference.
  • Techniques to detect potential model leakage.
  • Assessing risks of training data reconstruction.
  • Identifying data exfiltration paths in AI pipelines.
  • Monitoring data access and usage within AI systems.
  • Implementing controls against data leakage.

Module 4: Identifying Shadow AI and Unauthorized Access

  • Defining and recognizing “Shadow AI”.
  • Risks associated with unauthorized AI tools/APIs.
  • Methods for discovering unapproved AI usage.
  • Assessing access controls for AI models and data.
  • Auditing API usage logs for anomalies.
  • Policy development for acceptable AI use.

Module 5: Adversarial ML Tactics (Red/Blue Teaming Concepts)

  • Introduction to adversarial machine learning.
  • Understanding evasion, poisoning, and extraction attacks.
  • Conceptualizing Red Team exercises for AI.
  • Blue Team strategies for defending AI models.
  • Assessing model robustness against adversarial examples.
  • Evaluating defenses implemented in AI systems.

Module 6: AI Risk Reporting and Mitigation Strategies

  • Developing AI-specific risk assessment methodologies.
  • Creating effective risk dashboards for stakeholders.
  • Prioritizing internal AI security vulnerabilities.
  • Communicating AI risks to technical and non-technical audiences.
  • Recommending security controls and mitigation techniques.
  • Tracking remediation efforts and reassessing risks.

Exam Domains:

  1. Internal AI Threat Landscape Analysis
  2. AI System Component Security Evaluation
  3. Data Privacy and Confidentiality Auditing in AI
  4. Unauthorized AI Usage Detection Techniques
  5. Adversarial Attack Simulation and Defense Principles
  6. AI Security Risk Visualization and Communication

Course Delivery: The course is delivered through a combination of lectures and interactive discussions, facilitated by experts in AI security assessment. Participants will have access to online resources, including readings and case studies.

Assessment and Certification: Participants will be assessed through quizzes and assignments. Upon successful completion of the course requirements and exam, participants will receive an AI Internal Security Assessor (AIISA) certificate.

Question Types:

  • Multiple Choice Questions (MCQs)
  • True/False Statements
  • Scenario-based Questions
  • Fill in the Blank Questions
  • Matching Questions (Matching concepts or terms with definitions)
  • Short Answer Questions

Passing Criteria: To pass the AI Internal Security Assessor (AIISA) Certification Training exam, candidates must achieve a score of 70% or higher.

Specialize in securing internal AI systems. Enroll in the AIISA program today to gain critical skills for assessing and mitigating internal AI threats.

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