What Does Threat Modeling Look Like for AI in 2025? STRIDE vs OCTAVE vs AI-Specific

• Virtual Cyber Labs
• 04 Jun, 2025
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• 8 Mins Read

What Does Threat Modeling Look Like for AI in 2025? STRIDE vs OCTAVE vs AI-Specific

Introduction to Threat Modeling for AI

Threat modeling is a structured approach to identifying, assessing, and mitigating security risks in systems. As artificial intelligence (AI) systems become integral to industries like healthcare, finance, and autonomous vehicles, securing them against threats is critical. Unlike traditional software, AI systems introduce unique vulnerabilities, such as adversarial attacks, data poisoning, and model inversion, necessitating specialized threat modeling approaches.

This dives into threat modeling for AI, comparing three frameworks: STRIDE, OCTAVE, and AI-specific threat modeling. We’ll explore their methodologies, provide hands-on examples, and discuss their applicability to AI systems. Whether you’re a cybersecurity professional, AI developer, or business leader, this guide will equip you with actionable insights to secure AI deployments.

Why Threat Modeling Matters for AI

AI systems are complex, combining data pipelines, machine learning models, and deployment environments. These components introduce risks that traditional threat modeling may not fully address. For instance, an attacker could manipulate training data to bias a model or exploit inference APIs to extract sensitive information. Threat modeling for AI ensures these risks are identified early, enabling proactive mitigation.

Key reasons to prioritize threat modeling for AI include:

• Data Sensitivity: AI systems often process sensitive data, making them targets for data breaches.
• Adversarial Threats: Techniques like adversarial examples can deceive AI models, leading to incorrect outputs.
• Regulatory Compliance: Regulations like GDPR and CCPA mandate robust security for AI systems handling personal data.
• Business Impact: A compromised AI system can lead to financial losses, reputational damage, or safety risks.

Understanding Threat Modeling Frameworks

What is STRIDE?

STRIDE is a widely used threat modeling framework developed by Microsoft. It categorizes threats into six types: Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service (DoS), and Elevation of Privilege. STRIDE is system-agnostic, making it adaptable but not tailored specifically for AI.

Pros:

• Simple and structured.
• Applicable to various system components (e.g., APIs, databases).
• Well-documented with extensive community support.

Cons:

• Lacks AI-specific threat categories (e.g., adversarial attacks).
• May require customization for AI systems.

What is OCTAVE?

OCTAVE (Operationally Critical Threat, Asset, and Vulnerability Evaluation) is a risk-based framework developed by Carnegie Mellon University. It focuses on organizational risk management, emphasizing assets, vulnerabilities, and business impact.

Pros:

• Comprehensive risk assessment.
• Suitable for organizations with complex infrastructures.
• Prioritizes business objectives.

Cons:

• Less granular for technical components like AI models.
• Time-intensive for small teams.

AI-Specific Threat Modeling

AI-specific threat modeling frameworks, such as Microsoft’s AI Threat Modeling or OWASP’s AI Security Framework, address unique AI risks like data poisoning, model theft, and adversarial inputs. These frameworks extend traditional models to include AI-specific attack vectors.

Pros:

• Tailored to AI vulnerabilities.
• Covers emerging threats like model inversion.
• Integrates with existing frameworks like STRIDE.

Cons:

• Still evolving, with fewer established tools.
• Requires AI domain expertise.

Applying STRIDE to AI Systems

Step-by-Step STRIDE Process for AI

STRIDE involves analyzing a system through its six threat categories. Here’s how to apply STRIDE to an AI system, using a case study of a facial recognition system.

Step 1: Define the System

Create a Data Flow Diagram (DFD) to map the AI system’s components:

• Data Inputs: Image datasets, user uploads.
• Processes: Preprocessing, model inference.
• Data Stores: Training datasets, model weights.
• External Entities: Users, APIs.

Example DFD Command (using a tool like Microsoft Threat Modeling Tool):

# Install Microsoft Threat Modeling Tool (Windows)
# Download from: https://www.microsoft.com/en-us/securityengineering/sdl/threatmodeling
# Open tool and create DFD with:
# - External Entity: User
# - Process: Facial Recognition Model
# - Data Store: Image Dataset
# - Data Flow: Image Upload -> Model -> Output

Step 2: Identify Threats

For each component, apply STRIDE:

• Spoofing: An attacker impersonates a legitimate user to upload malicious images.
• Tampering: Modifying training data to bias the model.
• Repudiation: Lack of logging to trace malicious inputs.
• Information Disclosure: Exposing model weights via API vulnerabilities.
• DoS: Overloading the inference API with requests.
• Elevation of Privilege: Gaining unauthorized access to model training pipelines.

Step 3: Mitigate Threats

For each threat, propose mitigations:

• Spoofing: Implement strong authentication (e.g., OAuth).
• Tampering: Use data integrity checks (e.g., checksums).
• Information Disclosure: Encrypt model weights and use secure APIs.

Case Study Example:
In a facial recognition system, an attacker could tamper with training data to misidentify individuals. Mitigation includes:

• Hashing datasets: sha256sum dataset.csv
• Validating inputs: if not validate_image_format(image): reject_request()

Applying OCTAVE to AI Systems

OCTAVE Process for AI

OCTAVE focuses on organizational risk. Here’s how to apply it to an AI-driven healthcare diagnostics system.

Step 1: Identify Critical Assets

List assets critical to the AI system:

• Patient health data.
• Diagnostic model.
• Inference servers.

Step 2: Develop Risk Profiles

Assess threats and their impact:

• Threat: Data poisoning of patient records.
• Impact: Misdiagnosis, legal penalties.
• Likelihood: High, due to open data collection.

Step 3: Identify Vulnerabilities

Analyze system weaknesses:

• Unsecured data pipelines.
• Lack of anomaly detection in model outputs.

Step 4: Develop Mitigation Plans

Create strategies to reduce risks:

• Encrypt data pipelines: openssl enc -aes-256-cbc -in patient_data.csv -out encrypted_data.csv
• Implement anomaly detection: Use libraries like scikit-learn for outlier detection.

Case Study Example:
A healthcare AI system faced a data poisoning attack, leading to incorrect diagnoses. OCTAVE helped identify unencrypted data transfers as a vulnerability. Mitigation involved encrypting data with AES-256 and monitoring model outputs for anomalies.

Code Snippet (Anomaly Detection with scikit-learn):

from sklearn.ensemble import IsolationForest
import numpy as np

# Sample patient data
data = np.array([[...], [...], ...])  # Patient metrics
model = IsolationForest(contamination=0.1)
model.fit(data)

# Detect anomalies
anomalies = model.predict(data)  # Returns -1 for anomalies, 1 for normal

AI-Specific Threat Modeling

Building an AI-Specific Threat Model

AI-specific frameworks focus on unique threats like adversarial attacks and model extraction. Let’s apply this to a chatbot AI system.

Step 1: Identify AI-Specific Components

• Training Data: User queries dataset.
• Model: NLP model (e.g., BERT).
• Inference Pipeline: API serving predictions.

Step 2: List AI-Specific Threats

• Adversarial Inputs: Crafting queries to manipulate outputs.
• Model Inversion: Extracting training data from model outputs.
• Model Theft: Stealing model weights via API scraping.

Step 3: Mitigate AI-Specific Threats

• Adversarial Inputs: Use adversarial training to make models robust.from tensorflow.keras.models import load_model from cleverhans.tf2.attacks import fast_gradient_method import tensorflow as tf model = load_model('chatbot_model.h5') x = tf.convert_to_tensor(user_input) adversarial_x = fast_gradient_method(model, x, eps=0.1, norm=np.inf)
• Model Inversion: Limit API output granularity.
• Model Theft: Rate-limit APIs and use watermarking.

Case Study Example:
A chatbot was attacked with adversarial inputs, causing inappropriate responses. The team used adversarial training to improve robustness, reducing attack success by 80%.

Comparing STRIDE, OCTAVE, and AI-Specific Frameworks

When to Use:

• STRIDE: For teams familiar with traditional cybersecurity, needing quick threat identification.
• OCTAVE: For organizations prioritizing business risk and compliance.
• AI-Specific: For AI developers tackling unique threats like adversarial attacks.

Practical Tools and Commands

STRIDE Tools

• Microsoft Threat Modeling Tool: Free, supports DFD creation.# Install (Windows) winget install Microsoft.ThreatModelingTool
• OWASP Threat Dragon: Open-source alternative for DFDs.

OCTAVE Tools

• OCTAVE Allegro Worksheets: Available from Carnegie Mellon’s SEI website.
• Risk Management Software: Tools like RiskLens integrate OCTAVE principles.

AI-Specific Tools

• CleverHans: For adversarial attack simulation.pip install cleverhans
• Adversarial Robustness Toolbox (ART): For testing AI model security.pip install adversarial-robustness-toolbox

FAQ:

1. What is threat modeling for AI?

Threat modeling for AI is the process of identifying, assessing, and mitigating security risks specific to AI systems, including data, models, and deployment environments.

2. How does STRIDE differ from AI-specific threat modeling?

STRIDE is a general framework focusing on six threat categories, while AI-specific threat modeling addresses unique AI risks like adversarial attacks and model inversion.

3. Is OCTAVE suitable for small AI startups?

OCTAVE is comprehensive but complex, making it less ideal for small teams. STRIDE or AI-specific frameworks are more practical for startups.

4. What are common AI-specific threats?

Common threats include data poisoning, adversarial inputs, model theft, and model inversion attacks.

5. How can I test my AI model for adversarial attacks?

Use tools like CleverHans or ART to simulate adversarial inputs and evaluate model robustness.

6. Do I need specialized tools for AI threat modeling?

While general tools like Microsoft Threat Modeling Tool work, AI-specific tools like ART provide better support for AI vulnerabilities.

7. How often should I perform threat modeling for AI systems?

Perform threat modeling during system design, after major updates, or when new threats emerge (e.g., annually or post-incident).

Conclusion

Threat modeling for AI is essential to secure complex systems against evolving threats. STRIDE offers a simple, component-focused approach but requires customization for AI. OCTAVE excels in organizational risk management but may be overkill for technical teams. AI-specific threat modeling addresses unique AI risks, making it ideal for developers tackling adversarial attacks or model theft.

By combining these frameworks with practical tools like CleverHans and Microsoft Threat Modeling Tool, organizations can build robust AI systems. Start with a clear understanding of your system’s components, apply the appropriate framework, and regularly update your threat model to stay ahead of attackers.

Want to Dive Deeper into AI Security?

AI is transforming cybersecurity and vice versa. If you’re interested in exploring more insights, practical guides, and real-world case studies around AI in security, check out our other blogs:

• Breaking AI Defenses: Attacking Safety Layers & Fine-Tuned Filters
• Top 10 Ways GenAI Boosts SIEM, SOAR & EDR Performance
• Offensive AI Recon: Master Metadata & API Security Testing
• 10 Powerful Ways to Summarize MITRE ATT&CK Threat Vectors with ChatGPT