AI Security Risks: Comprehensive Assessment Framework

Traditional security approaches are insufficient for AI implementations. Organizations have rapidly adopted AI technologies without fully understanding the associated risks or implementing appropriate safeguards. This guide provides a structured framework for assessing AI security risks.

The Growing AI Security Challenge

The rush to deploy AI capabilities has outpaced security considerations in many organizations. Common gaps include:

• Undefined ownership of AI security responsibilities
• Missing policies for AI usage and data handling
• Inadequate monitoring of AI system behavior
• Unclear incident response procedures for AI failures
• Limited understanding of AI-specific attack vectors

Key Risk Categories

Prompt Injection and Data Extraction

Attackers manipulate AI inputs to bypass controls, extract sensitive data, or cause unintended actions. These attacks exploit the fundamental nature of how language models process instructions.

Training Data Poisoning and Model Theft

Adversaries may attempt to corrupt training data to influence model behavior or steal proprietary models representing significant intellectual property investments.

Integration Vulnerabilities

AI systems rarely operate in isolation. Integration points with databases, APIs, and other systems create attack surfaces that traditional security may not adequately address.

Output Manipulation and Misuse

AI outputs may be manipulated or misused, leading to misinformation, fraud, or other harmful outcomes that damage reputation and trust.

Assessment Frameworks

OWASP LLM Top 10 2025

The OWASP Large Language Model Top 10 addresses ten critical risks:

1. Prompt Injection - Direct and indirect manipulation of model inputs
2. Insecure Output Handling - Insufficient validation of AI-generated content
3. Training Data Poisoning - Corruption of training datasets
4. Model Denial of Service - Resource exhaustion attacks
5. Supply Chain Vulnerabilities - Third-party component risks
6. Sensitive Information Disclosure - Unintended data leakage
7. Insecure Plugin Design - Vulnerable extensions and integrations
8. Excessive Agency - Overly permissive model capabilities
9. Overreliance - Inappropriate trust in AI outputs
10. Model Theft - Unauthorized extraction of model capabilities

NIST AI Risk Management Framework

The NIST AI RMF encompasses four core functions:

1. Govern - Establish policies, roles, and accountability
2. Map - Understand context, stakeholders, and dependencies
3. Measure - Assess risks and monitor performance
4. Manage - Prioritize and address identified risks

This framework addresses broader organizational risk contexts beyond technical vulnerabilities.

Common Implementation Scenarios

Shadow AI Usage

Employees using unauthorized AI tools create uncontrolled data exposure risks. Assessment must identify shadow AI usage and establish appropriate governance.

Customer-Facing Chatbots

Public-facing AI interfaces represent significant attack surfaces requiring prompt injection defenses, output validation, and careful scope limitation.

Custom Applications with RAG

Retrieval-Augmented Generation applications introduce unique risks around data retrieval, context injection, and knowledge base security.

AI-Enhanced Development Tools

Code generation and development assistance tools may introduce vulnerabilities, expose proprietary code, or create supply chain risks.

Comprehensive Assessment Process

Discovery Phase

• Inventory all AI systems and usage patterns
• Document data flows and integration points
• Identify stakeholders and responsibilities
• Classify systems by criticality and sensitivity

Risk Assessment Phase

• Evaluate against OWASP LLM Top 10
• Apply NIST AI RMF functions
• Assess organization-specific risk scenarios
• Review governance and oversight mechanisms

Controls Evaluation Phase

• Assess existing security controls effectiveness
• Identify gaps in AI-specific protections
• Evaluate monitoring and detection capabilities
• Review incident response procedures

Gap Analysis and Recommendations

• Prioritize risks by likelihood and impact
• Develop remediation roadmap
• Provide implementation guidance
• Integrate findings into security program

Optional Offensive Testing

• Prompt injection and jailbreak attempts
• Data extraction testing
• Integration vulnerability exploitation
• Model manipulation assessment

Building AI Security Maturity

AI security is not a one-time assessment but an ongoing program. Organizations should:

• Establish AI governance structures and policies
• Implement continuous monitoring for AI systems
• Conduct regular reassessments as AI usage evolves
• Train staff on AI-specific security considerations
• Maintain incident response capabilities for AI failures

Ready to assess your AI security posture? Contact Breach Craft for a comprehensive AI security risk assessment. Learn more about why Breach Craft for AI security assessments.