What is AI Threat Modeling?

What is AI Threat Modeling?

AI Threat Modeling is the structured practice of identifying potential threats to AI systems, understanding how those threats could be exploited, assessing their potential impact, and determining appropriate countermeasures. It adapts the established discipline of threat modeling from cybersecurity and applies it to the unique characteristics and vulnerabilities of artificial intelligence systems.

Traditional threat modeling asks: What are we building? What can go wrong? What are we going to do about it? AI threat modeling asks these same questions but extends them to cover risks that do not exist in conventional software — data poisoning, adversarial inputs, model theft, prompt injection, training data leakage, and the emergent behaviours that arise from machine learning systems.

Why AI Threat Modeling Matters

AI systems introduce fundamentally different security challenges compared to traditional software:

• Data dependency: AI models are shaped by their training data, making the data pipeline a critical attack surface that does not exist in rule-based software.
• Probabilistic behaviour: AI systems produce outputs based on statistical patterns rather than deterministic rules, making their behaviour inherently less predictable and harder to fully test.
• Learned vulnerabilities: AI models can learn biases, weaknesses, and exploitable patterns from their training data without their developers being aware.
• Attack surface expansion: AI systems are vulnerable to traditional software attacks plus an entirely new category of AI-specific attacks.

For businesses, failing to threat-model AI systems means operating with blind spots. You may have excellent traditional security practices while being completely exposed to AI-specific threats that could compromise your models, leak your data, or cause your AI systems to behave in harmful ways.

The AI Threat Modeling Process

Step 1: System Decomposition

Begin by mapping every component of your AI system:

• Data sources: Where does training data come from? How is it collected, stored, and processed? Who has access?
• Training pipeline: How are models trained? What infrastructure is used? Who manages the training process?
• Model serving: How is the model deployed? What APIs expose it? Who can access it?
• Integration points: How does the AI system interact with other business systems, databases, and external services?
• Human touchpoints: Where do humans interact with the system as operators, reviewers, or end users?

Document these components in a system diagram that shows data flows, trust boundaries, and access controls.

Step 2: Threat Identification

Systematically identify threats to each component using AI-specific threat categories:

Data Threats

• Training data poisoning — corrupting the data used to build the model
• Data exfiltration — extracting sensitive information from training data through the model
• Data pipeline compromise — attacking the infrastructure that collects and processes training data
• Label manipulation — corrupting the labels or annotations on training data

Model Threats

• Adversarial attacks — crafting inputs that cause the model to make errors
• Model extraction — stealing the model through systematic querying
• Model inversion — recovering training data from the model's outputs
• Backdoor insertion — embedding hidden malicious behaviour in the model

Infrastructure Threats

• Prompt injection — manipulating AI behaviour through crafted inputs
• Supply chain attacks — compromising third-party models, libraries, or tools
• Compute infrastructure attacks — targeting the servers, GPUs, and cloud resources used for AI
• API abuse — exploiting the AI system's interfaces beyond intended use

Operational Threats

• Insider threats — authorised personnel misusing access to AI systems or data
• Drift and degradation — model performance declining over time due to changing data patterns
• Misuse — legitimate users employing the AI system for harmful purposes
• Regulatory non-compliance — AI system behaviour that violates applicable laws or standards

Step 3: Risk Assessment

For each identified threat, assess:

• Likelihood: How probable is this attack given your threat environment, attacker motivation, and current defences?
• Impact: What would be the business consequence if this threat materialised — financial loss, reputational damage, regulatory penalties, safety harm?
• Detectability: How quickly would you know if this attack occurred? Some AI attacks, like data poisoning, can go undetected for extended periods.

Use these factors to prioritise threats, focusing security investment on the highest-risk items first.

Step 4: Countermeasure Planning

For each prioritised threat, define countermeasures:

• Prevention: Controls that stop the threat from materialising, such as input validation, access controls, and data quality checks.
• Detection: Monitoring and alerting capabilities that identify attacks in progress or after the fact, such as anomaly detection, query monitoring, and output analysis.
• Response: Procedures for containing and recovering from successful attacks, including model rollback, incident response plans, and communication protocols.
• Recovery: Capabilities for restoring normal operations after an incident, including backup models, clean training data reserves, and failover procedures.

Step 5: Continuous Review

AI threat models are living documents. Review and update them when:

• New AI models or features are deployed
• The threat landscape evolves with new attack techniques
• Incidents occur that reveal previously unidentified threats
• Regulatory requirements change
• Business context shifts, such as entering new markets or serving new customer segments

AI Threat Modeling Frameworks

Several frameworks can guide AI threat modeling:

• STRIDE for ML: Adapts Microsoft's STRIDE framework (Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, Elevation of Privilege) to machine learning contexts.
• ATLAS (Adversarial Threat Landscape for AI Systems): Developed by MITRE, ATLAS catalogues known adversarial techniques against AI systems in a format similar to the MITRE ATT&CK framework for cybersecurity.
• OWASP Machine Learning Security Top 10: Identifies the most critical security risks in machine learning applications.

AI Threat Modeling in Southeast Asia

Organisations in Southeast Asia face unique threat modeling considerations:

Diverse Regulatory Environment

Different ASEAN countries have different regulatory requirements for AI. A threat model for a regional deployment must account for compliance risks across multiple jurisdictions, including Singapore's AI governance frameworks, Indonesia's data protection laws, Thailand's AI ethics guidelines, and emerging regulations elsewhere in the region.

Multilingual Attack Surface

AI systems operating in multiple languages face threats in each language. Adversarial attacks, prompt injections, and content manipulation may be more effective in languages where AI safety research and defensive tooling are less mature.

Growing Attack Sophistication

As AI adoption grows across the region, so does the sophistication of AI-targeted attacks. Financial institutions, government agencies, and technology companies in ASEAN markets are increasingly attractive targets for adversaries who understand AI-specific vulnerabilities.

Resource Constraints

Many organisations in the region, particularly those outside Singapore and major metropolitan centres, may lack dedicated AI security expertise. Threat modeling frameworks provide a structured approach that can be followed by general security professionals, with targeted specialist input where needed.