Preparedness depends on an understanding of potential threats. AI threat modeling helps organizations anticipate and mitigate potential threats to AI systems, including adversarial attacks, data poisoning and model stealing. AI threat modeling is foundational to developing and deploying safe, reliable and secure AI systems. This article explores key areas of AI threat modeling, and presents a simple framework for addressing the security, privacy and ethical challenges that AI systems pose.
What Is AI Threat Modeling?
AI systems have vulnerabilities that can be exploited. AI threat modeling provides a structured approach for identifying, assessing and minimizing the security, privacy and ethical risks inherent in these systems. AI threat modeling is especially valuable in protecting machine learning systems used in predictive analytics, since these systems drive crucial business operations. As AI adoption accelerates, AI threat modeling is becoming crucial for ensuring the safe and ethical use of AI technology.
Core Considerations of AI Threat Modeling
AI threat modeling addresses key security and ethical risk areas, helping organizations identify and mitigate vulnerabilities before they can be exploited.
Data integrity and security
Maintaining the integrity of the data used during model training and fine-tuning is a primary focus of AI threat modeling. High-quality data is an essential ingredient for building models that generate accurate, reliable outputs. However, adversarial machine learning attacks can derail AI models during development and deployment by targeting training data. Data poisoning is a primary example. This adversarial attack involves corrupting the training data by introducing inaccurate, biased or altered data. Models trained on poisoned data will produce unreliable outputs.
Privacy attacks can also compromise the security of the data used during the training phase, usually occurring after a model has been deployed. For example, in a data reconstruction attack, malicious actors will reconstruct training samples by exploiting a neural network’s propensity to memorize training data. Using direct prompt injections, attackers can request that the model disclose confidential information that was used during training.
Model resilience
Building resilience into machine learning (ML) models enables them to more easily adapt and recover from adversarial attacks. Several strategies can be deployed to build model resilience. One technique is to use adversarial examples during training. This helps the model recognize and handle corrupted data without affecting performance.
AI infrastructure includes multiple layers: the foundational hardware, software and cloud services, the model layer and the applications layer. Each layer has a unique set of vulnerabilities that must be understood and addressed. An example at the model layer is model stealing, which involves trying to steal a model’s architecture, parameters or training data. Resilience against all threats is key to any AI model.
Potential fallout from a compromised AI model
When compromised, machine learning models can behave unpredictably, leading to real-world consequences. AI threat modeling assesses these risks during an AI system's development and deployment. Examples where AI data analysis can prevent problems include driver-assistance interference, cybersecurity detection evasion, and financial loss avoidance from poor investments.
Steps to Create an AI Threat Modeling Framework
An AI threat modeling framework offers a systematic approach to identify, evaluate and mitigate AI-specific security threats. While the exact approach used will depend on the organization’s specific situation and needs, the following framework provides a basic methodology for developing and deploying more robust, secure AI solutions.
Inventory your assets
AI threat modeling begins with an inventory of the assets that need to be protected. This includes the components included in the infrastructure, model and application layers. At this stage, it’s important to identify the various groups with an interest in the model — including business stakeholders, end users and malicious actors who would benefit from launching an adversarial attack.
Identify and analyze potential threats
Once the AI system’s components have been inventoried and its stakeholders identified, it’s much easier to understand the risks that could threaten the security and safety of the AI system. Threats are present throughout the machine learning pipeline. Examples include attacks that target data integrity and security, such as model inversion and membership inference attacks or the exploitation of technical vulnerabilities in the model's design or implementation. Teams can prioritize which vulnerabilities should receive priority by organizing potential threats by their likelihood of occurrence, their level of severity and how vulnerable the AI systems are to these types of threats.
Develop mitigation strategies and controls
Mitigation strategies and controls are used to reduce or eliminate the vulnerabilities identified above. Encryption of model parameters and training data, differential privacy and adversarial training can also be used to build more resilient and secure AI systems.
Implement continuous monitoring
AI threat modeling is an iterative process. As AI systems evolve, new vulnerabilities and attack methods emerge. Regularly updating threat model and mitigation strategies keeps models, data and infrastructure secure.
Harden Your AI Security with Snowflake
The Snowflake AI Data Cloud helps organizations strengthen the security of their models and data. Snowflake provides industry-leading security features built to protect your organization’s models and data. With Snowflake for generative AI and ML, you can securely build and deploy advanced AI solutions with a robust security foundation and role-based access control (RBAC) for data, models and applications. Mitigate risks throughout the AI lifecycle effectively with Snowflake.