Module 8: Security, Governance, and Responsible AI

8.1 Introduction to AI Security and Governance

The Security Imperative for Agentic AI

Agentic AI systems present unique security challenges that extend beyond traditional software security concerns. These systems are designed to act autonomously, make decisions, access resources, and potentially control other systems—capabilities that significantly expand their potential impact and attack surface. As agentic AI systems become more powerful and widespread, ensuring their security becomes increasingly critical.

Several factors make security particularly important for agentic AI systems:

• Expanded Capabilities: Agents can access tools, APIs, and resources, potentially with significant privileges.
• Autonomous Operation: Agents may operate without direct human supervision, making real-time security monitoring essential.
• Complex Interactions: Multi-agent systems involve intricate interactions that can be difficult to secure comprehensively.
• Novel Attack Vectors: Agents introduce new attack surfaces, including prompt injection, model manipulation, and tool misuse.
• High-Stakes Applications: Agents may be deployed in sensitive domains where security breaches could have serious consequences.
• Evolving Landscape: The rapid development of AI capabilities requires continuous adaptation of security measures.

Governance and Responsible AI

Beyond technical security measures, effective governance frameworks and responsible AI practices are essential for ensuring that agentic AI systems operate safely, ethically, and in compliance with relevant regulations. Governance encompasses the policies, processes, and organizational structures that guide the development, deployment, and operation of AI systems.

Key aspects of AI governance include:

• Risk Management: Identifying, assessing, and mitigating risks associated with AI systems.
• Ethical Guidelines: Establishing principles and standards for ethical AI development and use.
• Accountability Mechanisms: Defining responsibilities and ensuring accountability for AI outcomes.
• Transparency Requirements: Setting expectations for explainability and documentation.
• Compliance Frameworks: Ensuring adherence to relevant laws, regulations, and industry standards.
• Oversight Structures: Creating processes for review, approval, and monitoring of AI systems.

Security Threats to Agentic AI Systems

Agentic AI systems face a range of security threats, including both traditional cybersecurity risks and AI-specific vulnerabilities:

1. Prompt Injection Attacks

Attempts to manipulate agent behavior through carefully crafted inputs:

• Direct Injection: Explicitly instructing the agent to perform unauthorized actions.
• Indirect Injection: Subtly influencing agent behavior through context manipulation.
• Jailbreaking: Bypassing safety measures to access restricted capabilities.
• Role-Playing Exploits: Tricking the agent into assuming a role with different constraints.

2. Data Poisoning

Compromising agent behavior through manipulation of training or operational data:

• Training Data Poisoning: Introducing malicious examples during model training.
• Retrieval Augmentation Poisoning: Manipulating documents or knowledge sources used by agents.
• Memory Poisoning: Corrupting agent memory or context to influence future decisions.
• Tool Output Manipulation: Providing misleading information through compromised tools.

3. Tool and API Misuse

Exploiting agent access to external tools and APIs:

• Privilege Escalation: Using agent capabilities to gain unauthorized access.
• Resource Abuse: Exploiting agent access to consume excessive resources.
• Data Exfiltration: Using agent capabilities to extract sensitive information.
• Lateral Movement: Leveraging agent access to reach other systems or resources.

4. Model Extraction and Inversion

Attempts to extract model information or training data:

• Model Extraction: Reconstructing model parameters or architecture through queries.
• Training Data Extraction: Inferring training data through careful probing.
• Membership Inference: Determining whether specific data was used in training.
• Property Inference: Extracting statistical properties of training data.

5. Traditional Security Vulnerabilities

Conventional cybersecurity threats that affect the broader system:

• Authentication Bypasses: Circumventing access controls to use agent capabilities.
• Infrastructure Vulnerabilities: Exploiting weaknesses in underlying systems.
• Supply Chain Attacks: Compromising dependencies or components used by agents.
• Social Engineering: Manipulating humans with access to agent systems.

Security and Governance Frameworks

Several frameworks provide guidance for securing AI systems and implementing effective governance:

1. NIST AI Risk Management Framework (AI RMF)

The National Institute of Standards and Technology's framework for managing AI risks:

• Govern: Establishing governance structures and processes.
• Map: Identifying and documenting context, capabilities, and risks.
• Measure: Assessing risks through testing, evaluation, and verification.
• Manage: Prioritizing and addressing risks through controls and monitoring.

2. OWASP Top 10 for LLM Applications

The Open Web Application Security Project's list of critical security risks for LLM applications:

• LLM01: Prompt Injection
• LLM02: Insecure Output Handling
• LLM03: Training Data Poisoning
• LLM04: Model Denial of Service
• LLM05: Supply Chain Vulnerabilities
• LLM06: Sensitive Information Disclosure
• LLM07: Insecure Plugin Design
• LLM08: Excessive Agency
• LLM09: Overreliance
• LLM10: Model Theft

3. EU AI Act

The European Union's regulatory framework for AI systems: