Do you authenticate and verify your ML model's feedback?
Explanation
Model integrity is the concern: reviewers want to know whether you authenticate and verify the feedback used to train, update, or improve your machine learning models.
In ML systems, 'feedback' refers to the data that is used to refine models over time. This could include user interactions, corrections to model outputs, or other data that helps the model learn and improve. Authentication and verification of this feedback means ensuring that the feedback is legitimate, comes from authorized sources, and hasn't been tampered with.
Why this matters for security:
- Model Skewing/Poisoning: Without proper authentication and verification, malicious actors could inject harmful feedback that deliberately skews your model's behavior (known as a 'poisoning attack'). For example, they could train a content moderation AI to ignore certain types of harmful content.
- Data Integrity: Ensuring that the feedback data maintains its integrity is crucial for maintaining model accuracy and preventing manipulation.
- Authorized Sources: Verifying that feedback comes from authorized users or systems helps prevent unauthorized influence on your models.
To best answer this question, you should describe:
- The specific mechanisms you use to authenticate the sources of feedback (e.g., user authentication, API keys)
- How you verify the integrity and validity of the feedback data
- Any monitoring systems you have in place to detect anomalous feedback patterns
- How you protect against feedback manipulation attacks
- Any human review processes for feedback data
Guidance
Looking for authentication and verification of feedback of the ML model to address the risk of model skewing.
Example Responses
Example Response 1
Yes, we implement a comprehensive authentication and verification system for all ML model feedback All feedback sources must authenticate using our OAuth 2.0 system with MFA requirements Before incorporation into our training pipeline, feedback data undergoes multiple verification steps: 1) Source verification to confirm it comes from authorized users/systems, 2) Integrity checks using cryptographic signatures to detect tampering, 3) Anomaly detection to identify statistically unusual feedback patterns that might indicate poisoning attempts, and 4) Regular human review of feedback samples by our data science team Additionally, we maintain an immutable audit log of all feedback submissions and their sources For critical models, we implement a quarantine period where new feedback is monitored in a sandbox environment before being used for model updates in production.
Example Response 2
Yes, our ML feedback authentication and verification process operates on three levels First, all feedback channels require authentication through our SSO system, with different permission levels determining who can provide feedback to which models Second, we employ a technical verification framework that includes data validation (ensuring feedback meets expected formats and ranges), provenance tracking (maintaining a chain of custody for all feedback data), and automated outlier detection to flag potentially manipulated inputs Third, we use a human-in-the-loop approach where our ML engineers review aggregated feedback metrics weekly and manually inspect any flagged anomalies We also maintain separate verification procedures for internal feedback (from our team) versus external feedback (from customers or partners), with stricter controls on external sources.
Example Response 3
No, we currently do not have a formal authentication and verification system specifically for our ML model feedback While users must log into our platform to interact with our services, we don't have additional verification mechanisms to ensure the integrity of feedback data before it's incorporated into our model training We recognize this as a potential security gap that could allow for model poisoning or manipulation We're currently developing a feedback verification framework that will include source authentication, data validation, and anomaly detection capabilities, which we expect to implement within the next quarter In the interim, we mitigate risk by having our data science team manually review feedback data before incorporating it into model retraining cycles.
Context
- Tab
- AI
- Category
- AI Machine Learning
Related questions
- Do you separate ML training data from your ML solution data?
- Is your ML training data vetted, validated, and verified before training the solution's AI model?
- Is your ML training data monitored and audited?
- Have you limited access to your ML training data to only staff with an explicit business need?
- Have you implemented adversarial training or other model defense mechanisms to protect your ML-related features?
- Do you make your ML model transparent through documentation and log inputs and outputs?
