AI Security Risks: Comprehensive Assessment Framework
The Growing AI Security Challenge
Artificial Intelligence is rapidly transforming how organizations operate, creating new capabilities but also introducing novel security risks that traditional security frameworks weren't designed to address. From customer-facing chatbots to internal productivity tools leveraging large language models (LLMs), AI implementations create unique vulnerabilities that require specialized assessment approaches.
The challenge is particularly acute because AI adoption has often outpaced security considerations. Many organizations have embraced AI technologies without fully understanding the associated risks or implementing appropriate safeguards. Whether it's unvetted shadow AI usage by employees, customer-facing generative AI applications, or custom AI solutions with access to sensitive data, these technologies introduce security concerns that extend beyond traditional application security boundaries.
Addressing these risks requires a systematic approach that builds on established security principles while incorporating AI-specific considerations.
Understanding AI Security Risk Categories
AI systems introduce several distinct risk categories that organizations must address:
Prompt Injection and Data Extraction
AI systems, particularly large language models, are vulnerable to adversarial inputs designed to manipulate their behavior. Through carefully crafted prompts, attackers can potentially:
Bypass security controls and restrictions
Extract sensitive information contained in training data
Access confidential information from connected data sources
Manipulate the AI to perform unauthorized actions
Execute code or commands through the AI interface
Training Data Poisoning and Model Theft
The models themselves represent valuable intellectual property and can be compromised through:
Adversarial manipulation of training data
Unauthorized access to model weights and parameters
Model inversion attacks to reconstruct training data
Extraction of proprietary algorithms and approaches
Supply chain compromises in model development
Integration Vulnerabilities
Many AI risks stem from how systems are integrated with other applications and data sources:
Insecure API implementations connecting to AI services
Excessive permissions granted to AI systems
Inadequate monitoring of AI system behaviors
Improper handling of sensitive data during processing
Vulnerable third-party libraries and dependencies
Output Manipulation and Misuse
The outputs of AI systems can create risks if not properly validated:
Generation of harmful, illegal, or biased content
Production of misleading or intentionally false information
Unauthorized disclosure of sensitive information in responses
Legal and compliance violations in AI-generated content
Reputational damage from inappropriate AI responses
Framework-Based Assessment Approaches
To address these complex and evolving risks, organizations need structured frameworks specifically designed for AI security. Two leading frameworks provide comprehensive guidance:
OWASP LLM Top 10 2025
The OWASP LLM Top 10 2025 identifies the most critical security risks for applications utilizing large language models, providing a focused approach to addressing the most urgent concerns.
1. LLM01: Prompt Injection
Adversarial prompts that manipulate the model into performing unintended actions or revealing sensitive information. This includes:
Direct Prompt Injection: Where malicious instructions are inserted directly
Indirect Prompt Injection: Where the model processes content containing hidden malicious instructions
Effective controls include input validation, context boundary enforcement, and prompt design best practices.
2. LLM02: Insecure Output Handling
Failing to properly validate, sanitize, or handle AI-generated outputs, potentially leading to downstream vulnerabilities like XSS, SSRF, or code injection when outputs are used in application logic.
Mitigation requires output validation, content filtering, and careful integration design.
3. LLM03: Training Data Poisoning
Compromising model behavior through manipulation of training data, potentially introducing backdoors or biases that can be exploited later.
Controls include training data validation, model evaluation for unexpected behaviors, and secure data sourcing practices.
4. LLM04: Model Denial of Service
Crafting inputs that consume excessive resources, potentially degrading service for legitimate users or increasing operational costs.
Protection requires input rate limiting, computational resource constraints, and monitoring for anomalous usage patterns.
5. LLM05: Supply Chain Vulnerabilities
Risks introduced through pre-trained models, third-party components, plugins, or data sources that may contain vulnerabilities or malicious code.
Mitigation requires vendor assessment, model provenance verification, and component security testing.
6. LLM06: Sensitive Information Disclosure
The risk of AI systems revealing confidential information, either from training data or connected data sources.
Controls include data minimization, content filtering for sensitive patterns, and regular testing for information leakage.
7. LLM07: Insecure Plugin Design
Vulnerabilities in how AI systems interact with plugins or extensions, potentially enabling unauthorized actions or data access.
Protection requires strict permission models, input/output validation, and secure plugin architecture design.
8. LLM08: Excessive Agency
Providing AI systems with excessive capabilities or permissions that extend beyond their required functionality, increasing potential impact if compromised.
Mitigation includes implementing least privilege principles, action validation, and clear permission boundaries.
9. LLM09: Overreliance
Placing excessive trust in AI outputs without appropriate verification, potentially leading to security or business decisions based on flawed information.
Controls include output verification, human oversight for critical decisions, and transparency about AI limitations.
10. LLM10: Model Theft
Unauthorized access to proprietary models, potentially compromising intellectual property or enabling adversaries to study the model for vulnerability identification.
Protection requires access controls, monitoring for extraction attempts, and model watermarking where applicable.
NIST AI Risk Management Framework (AI RMF)
The NIST AI Risk Management Framework provides a broader approach to AI risk that extends beyond security to include reliability, fairness, and governance considerations. The framework is structured around four core functions:
1. Govern
Establishing the organizational structures, policies, and processes needed for comprehensive AI risk management:
Defining AI governance roles and responsibilities
Developing AI-specific policies and standards
Establishing oversight mechanisms and review processes
Integrating AI risk management with enterprise risk frameworks
Ensuring compliance with relevant regulations and standards
2. Map
Identifying and documenting the context, capabilities, and potential impacts of AI systems:
Cataloging AI systems and use cases across the organization
Documenting data flows and integration points
Identifying stakeholders and their concerns
Mapping regulatory and compliance requirements
Assessing potential impacts on individuals and society
3. Measure
Evaluating and quantifying AI-specific risks using appropriate metrics and methods:
Analyzing technical vulnerabilities and security concerns
Assessing reliability, robustness, and resilience
Evaluating potential for bias and fairness issues
Measuring privacy impacts and data protection
Determining transparency and explainability levels
4. Manage
Implementing controls and processes to address identified risks:
Prioritizing risk treatment based on impact and likelihood
Implementing technical and procedural safeguards
Establishing monitoring and detection capabilities
Developing incident response procedures
Creating ongoing assessment and improvement processes
Common AI Implementation Scenarios and Risks
Different AI implementation patterns present distinct risk profiles that require targeted assessment approaches:
Shadow AI Usage
Scenario: Employees using publicly available AI tools like ChatGPT, Claude, or Gemini for work purposes without organizational oversight.
Key Risks:
Inadvertent sharing of sensitive information with external AI services
Intellectual property exposure through prompt inputs
Lack of monitoring or governance over AI interactions
Potential regulatory violations for regulated data
Inconsistent outputs affecting business decisions
Assessment Focus:
Discovery of unauthorized AI tool usage
Data handling practices when using external AI services
Awareness of security and compliance implications
Alternative approved solutions for legitimate use cases
Customer-Facing AI Chatbots
Scenario: Organizations deploying AI chatbots to handle customer inquiries, process requests, or provide product information.
Key Risks:
Prompt injection attacks against the chatbot interface
Excessive data access by the underlying AI system
Generation of inaccurate or harmful responses
Handling of sensitive customer information
Social engineering facilitated by conversational interfaces
Assessment Focus:
Input validation and prompt security controls
Authentication and access control mechanisms
Output filtering and content safety measures
Data handling practices and minimization
Monitoring and oversight capabilities
Custom Applications with RAG (Retrieval-Augmented Generation)
Scenario: Internal applications leveraging AI with access to corporate data sources through Retrieval-Augmented Generation techniques.
Key Risks:
Unauthorized data access through improper retrieval patterns
Inclusion of sensitive information in AI responses
Excessive permissions granted to AI components
Inadequate validation of retrieved content
Data leakage through model interactions
Assessment Focus:
Data access controls and permission boundaries
Retrieval system security and filtering
Output validation and sensitive data detection
Logging and monitoring of data access
Authentication and authorization models
AI-Enhanced Development Tools
Scenario: Development teams using AI coding assistants and other AI-enhanced development tools.
Key Risks:
Introduction of vulnerable code or logic flaws
Intellectual property leakage through code sharing
Overreliance on AI-generated code without review
Supply chain risks from AI-suggested dependencies
Exposure of architectural information or security controls
Assessment Focus:
Code review processes for AI-generated content
Security testing of AI-suggested implementations
Guidelines for appropriate prompting and usage
Intellectual property protection in development workflows
Integration with secure development lifecycle
Conducting an Effective AI Security Assessment
A comprehensive AI security assessment integrates elements from both the OWASP LLM Top 10 and NIST AI RMF, following a structured approach:
1. Discovery and Inventory
Identify all AI systems in use or development across the organization
Document integration points, data sources, and usage patterns
Classify systems based on data sensitivity and business criticality
Map technology stack and implementation approaches
Identify stakeholders and system owners
2. Risk Assessment
Evaluate each system against OWASP LLM Top 10 categories
Apply relevant NIST AI RMF measurements and metrics
Assess implementation-specific vulnerabilities
Consider business context and potential impact
Identify regulatory and compliance requirements
3. Controls Evaluation
Review existing security controls and their effectiveness
Assess governance structures and oversight mechanisms
Evaluate incident response capabilities for AI-specific scenarios
Review monitoring and detection capabilities
Assess supply chain security for models and components
4. Gap Analysis and Recommendations
Identify control gaps and deficiencies
Develop prioritized recommendations based on risk
Propose governance improvements and policy updates
Suggest technical controls and implementation approaches
Create a roadmap for ongoing improvement
5. Integration with Security Program
Align findings with existing security frameworks (e.g., NIST CSF, CIS Controls)
Incorporate AI security into broader security processes
Develop AI-specific policies and standards
Establish ongoing assessment and monitoring protocols
Define roles and responsibilities for AI security
6. Optional: Offensive Security Testing
For organizations seeking to validate their defenses beyond a standard gap assessment, we offer offensive security testing that actively tests AI implementations:
Prompt injection attacks against production interfaces
Data extraction attempt simulation
Jailbreak testing and restriction bypass attempts
Supply chain compromise simulation
Integration vulnerability exploitation
API security testing
This hands-on offensive testing provides evidence-based validation of your security controls against real-world attack techniques.
Building Effective AI Security Governance
Beyond point-in-time assessments, organizations need to establish ongoing governance for AI security:
Policy Development
Create AI-specific security policies that address:
Acceptable use guidelines for AI technologies
Data handling requirements for AI systems
Security requirements for AI development and deployment
Vendor assessment criteria for AI services
Incident response procedures for AI-specific events
Monitoring and Detection
Implement specialized monitoring for AI systems:
Anomalous usage patterns or unusual requests
Potential data exfiltration or leakage
Prompt injection attempts or manipulations
Unexpected model behaviors or outputs
Resource consumption and potential DoS conditions
Training and Awareness
Develop targeted education programs:
AI security awareness for all employees
Specialized training for AI developers and operators
Executive education on AI risks and governance
User guidance for interacting with AI systems
Incident recognition and reporting procedures
Moving Forward: Integrating AI Security into Your Program
As AI adoption continues to accelerate, organizations must incorporate AI security into their overall security strategy. Key steps include:
Inventory your AI footprint, including shadow AI usage, vendor solutions, and custom implementations
Assess your current risk using structured frameworks like OWASP LLM Top 10 and NIST AI RMF
Develop AI-specific security controls that address the unique risks these systems present
Integrate AI security into your existing security program and governance structures
Establish ongoing monitoring to detect emerging threats and vulnerabilities
Create clear policies for secure AI development, procurement, and usage
By taking a systematic, framework-based approach to AI security assessment, organizations can continue to leverage the benefits of AI while managing the associated risks.
Contact us today to discuss how a customized AI security assessment can help your organization identify and address the unique risks associated with artificial intelligence technologies.