Introduction: Why Threat Modeling Matters for AI Systems

‍

As organizations increasingly adopt AI and machine learning (ML) technologies, their threat landscape expands. AI systems are not just applications—they are dynamic entities with unique components like data pipelines, model training, and inference engines. Each stage presents its own vulnerabilities.

‍

Traditional threat modeling frameworks fall short when applied to AI, making it crucial to adapt these frameworks for AI-specific risks. In this blog, we’ll explore how ioSENTRIX helps organizations build robust AI threat models.

Key Components of AI Threat Modeling

‍

1. Data Ingestion and Preprocessing Risks

‍

AI systems rely on vast amounts of data, often collected from multiple sources. This makes them susceptible to:

‍

• Data Poisoning: Injecting malicious data to influence model outcomes.
• Unauthorized Access: Compromised data sources lead to unauthorized access to sensitive information.

‍

‍

2. Model Training and Development Risks

‍

The model development stage introduces risks tied to the core of AI functionality:

‍

• Adversarial Inputs During Training: Attackers craft training data that forces the model to learn incorrect behaviors.
• Bias Introduction: Training on biased data leads to flawed and potentially harmful outputs.

‍

3. Model Deployment and Inference Risks

‍

Once models are deployed, they become targets for attackers aiming to manipulate or extract their functionality:

‍

• Model Inference Attacks: Attackers deduce training data or replicate models.
• API Exploitation: Exposed APIs are gateways for unauthorized queries and manipulation.

Adapting Threat Modeling Frameworks for AI

ioSENTRIX leverages established frameworks like STRIDE and ATT&CK and adapts them to AI systems:

‍

• STRIDE for AI: Identifies threats like spoofing, tampering, and information disclosure in AI-specific contexts.
• MITRE ATT&CK for AI: Provides tactics and techniques attackers use against AI systems.

‍

1. Threat Enumeration

‍

We enumerate threats specific to your AI/ML pipeline. This includes everything from poisoned data entry points to insecure API endpoints.

‍

‍

2. Attack Surface Mapping

‍

Our approach maps the entire attack surface of your AI system, identifying all potential entry points for attackers.

‍

3. Risk Prioritization

‍

Not all threats are equal. ioSENTRIX uses risk scoring to prioritize threats based on their impact and likelihood.

Conclusion: Build Resilient AI with ioSENTRIX

Threat modeling for AI systems requires an evolved approach. By identifying and mitigating AI-specific risks, ioSENTRIX helps organizations protect their AI assets and ensure robust security.

‍

Ready to secure your AI systems? Contact us today for a tailored threat modeling assessment.

‍

‍

‍