What is AI data security?
AI data security is a specialized practice at the intersection of data protection and AI security that’s aimed at safeguarding data used in AI and machine learning (ML) systems. By protecting the underlying data, you can prevent breaches, unauthorized access, manipulation, and disruption to your production AI models and workflows.
This article breaks down why AI security matters now, walks you through the key principles and best practices to follow, and showcases how Wiz AI-SPM strengthens your organization's AI data security posture. Let’s dive in.
Why is AI data security necessary?
Data security has always been a top priority for executives, especially for data-intensive organizations, and with AI the stakes are even higher.
Traditionally, data security is necessary to:
Protect proprietary and sensitive information, including intellectual property, trade secrets, financial records, and personally identifiable information (PII).
Maintain customer trust, as any data breachーespecially if it involves customer dataーleads to loss of reputation and trust, ultimately impacting customer retention.
Ensure regulatory compliance against both existing (e.g., GDPR and HIPAA) and upcoming data protection laws and AI compliance regulations.
Guarantee business continuity to prevent attacks and make sure that the breaches that do occur have a minimal impact on business operations during mitigation.
Failing to protect your data systems can lead to risks such as data exposure, data breaches, social engineering, phishing attacks, ransomware, and data loss in the cloud.
When AI systems are integrated, they become a key differentiator, offering businesses a competitive advantage. Ensuring AI data security is then necessary for maintaining an edge in today's fast-paced technological ecosystem.
AI exacerbates the technical complexity of data security: With AI, there are larger data volumes, more diverse sources, an increased attack surface, non-deterministic outputs, and specialized AI threats (like a model’s vulnerability to adversarial attacks and its inherent bias).
Real-life examples of AI data breaches
Recent high-profile incidents demonstrate just how necessary securing AI systems against data exposure is, revealing critical security gaps that even tech giants are struggling to address:
SAP AI vulnerabilities: Critical weaknesses in SAP's AI infrastructure exposed sensitive data to potential unauthorized access, allowing attackers to access sensitive business operations and data managed by SAP systems.
NVIDIA AI framework bug: A flaw in NVIDIA's AI framework allowed container escape, threatening data integrity and security, with attackers gaining complete host takeover of machines.
Microsoft data exposure: Misconfigured cloud storage by Microsoft’s AI research team led to the accidental exposure of 38 terabytes of private data, including passwords, secret keys, and sensitive internal communications, creating a huge risk of exploitation.
Hugging Face infrastructure risks: Security risks in Hugging Face’s AI infrastructure showed how even widely used platforms can be compromised, affecting organizations that rely on this popular third-party provider for deploying their AI solutions.
With AI adoption skyrocketing, these real-world examples show that the risks to sensitive data have never been more pressing.
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Key principles of AI data security
When building secure AI systems, compliance by design is key. The General Data Protection Regulation (GDPR) provides a solid framework that’s useful when designing AI deployments. In Article 5, there are seven core principles for data protection, four of which are particularly relevant to AI data security. Here’s how they apply to AI:
| Core Principle | Definition | How it applies to AI | Example |
|---|---|---|---|
| Integrity and confidentiality | Protecting data from unauthorized access, alterations, or leaks | For AI, this could mean encrypting sensitive datasets during model training and ensuring least-privilege access control. | If patient data isn’t properly secured in a healthcare AI model, unauthorized parties could access or manipulate it, leading to major breaches or incorrect predictions. |
| Accuracy | Keeping data accurate and up-to-date to prevent faulty prediction | AI systems thrive on clean, precise data. If the data is outdated or incorrect, the models produce flawed results. | A financial AI system trained on outdated transaction data could lead to unreliable and suboptimal fraud detection or financial forecasting, both leading to significant losses. |
| Storage limitation | Storing data only as long as necessary for the intended purpose | AI models tend to devour large datasets, but holding on to data longer than needed introduces risk. Defining clear data-deletion policies ensures you’re minimizing risk while staying compliant. | Imagine an AI-driven customer sentiment analysis tool that stores historical training data indefinitely. Besides breaching data retention policies, this creates unnecessary exposure risks (and costs). |
| Accountability | Demonstrating compliance, ownership, and transparency | Organizations must be able to demonstrate compliance with AI data security practices, which means having proper audit trails. | In an e-commerce AI deployment, for example, logging every access and modification to training data can help pinpoint vulnerabilities and ensure security measures are followed. |
To uphold these principles across all AI systems, organizations should implement a strong AI governance framework within their AI risk management practice.
Best practices for AI data security
As we’ve seen, securing AI data pipelines requires building on top of traditional data security practices while adding layers specific to the unique challenges AI introduces.
Traditional security controls such as zero-trust access, data encryption, data masking, privacy policies, security awareness training, and regular security assessments all hold for AI systems. Read on for ways to incorporate these practices into AI environments:
1. Data access management
Because AI pipelines often involve large data transfers, preventing accidental exposure or unauthorized transfer of sensitive data is a must.
Techniques
Implement AI-aware data access policies, such as restrictions based on the AI model's stage or enforcing differential privacy, to ensure secure data handling during model training and deployment.
Use automated data classification to flag sensitive information in AI datasets.
To prevent data exfiltration incidents, monitor cloud environments via network detection solutions with data monitoring policies that uncover abnormal flow patterns and access.
2. Adversarial training
When it comes to AI, even small input modifications can cause drastic prediction errors. That’s why it’s essential to defend AI models against adversarial inputs designed to manipulate or mislead the model.
Techniques
Train models using adversarial input simulation to build resilience against these manipulations.
Implement gradient masking to make accessing gradients more difficult.
Experiment with defensive distillation to modify the model to make it less sensitive to manipulations in the input.
Find model vulnerabilities by simulating adversarial attacks.
3. Model evaluation
Regularly assess AI models for vulnerabilities and biases, both in the development and deployment phases, to ensure that AI models behave as expected.
Techniques
Validate all inputs against known safe data types and formats before they reach the model.
Perform bias audits to uncover systematic unfairness in your training data and model outputs.
Stress test the model’s performance under various data scenarios to ensure robustness.
4. Input validation
Validate incoming data to ensure it’s clean, trustworthy, and free of malicious content.
Techniques
Apply techniques like data sanitization to clean inputs and prevent injection attacks.
Employ anomaly detection tools to spot unusual or out-of-pattern inputs before they reach the model.
Perform boundary value checks to ensure inputs fall within acceptable ranges.
5. Secure model deployment
To prevent unauthorized access or manipulation, prioritize the security of model deployment.
Techniques
Containerize AI models to isolate them from other services and minimize attack surfaces.
Apply encryption to both models and their outputs to prevent exposure during inference.
Implement multi-factor authentication (MFA) for teams managing the model deployment pipeline.
Ensure API security (an especially relevant step for GenAI applications relying on third-party providers) via rate limiting, authentication, and encryption.
6. Model monitoring and auditing
Continuously monitor AI models post-deployment for suspicious activity as well as to ensure they don't drift or degrade.
Techniques
Use real-time anomaly detection to flag irregular behaviors or output patterns.
Schedule regular audits to track changes to data and models. (This is also useful for compliance.)
Introduce performance monitoring tools to ensure the model continues to function as expected in production.
Another key best practice? Encourage close collaboration between security teams and data science teams. By working together, they can integrate multi-layered security into the AI pipeline, reducing risks while maintaining model performance and reliability.
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Wiz AI-SPM for AI data security
It doesn’t have to be hard to minimize overhead and start securing AI systems quickly. As a cloud native application protection platform (CNAPP), Wiz offers a specialized AI security posture management solution integrated within our security platform: Wiz AI-SPM.
Wiz AI-SPM simplifies AI and machine learning security by offering three key functionalities:
Visibility via AI-BOM (AI bill of materials): Wiz provides a comprehensive view into every part of your AI pipeline including your data assets, transformations, and usage.
Risk assessment: Our all-in-one platform continuously evaluates AI pipelines for general risks as well as data-specific risks, such as unauthorized data access, adversarial inputs, and data poisoning attempts.
Proactive risk mitigation: Wiz automatically identifies, prioritizes, and mitigates vulnerabilities with real-time contextual insights, reducing the burden on SecOps teams.
An example of Wiz AI-SPM in action
Imagine your organization relies on a real-time AI inference system for a critical business operation, such as a fraud-detection system. A data poisoning attack compromises some of the transactional data, leading the model to provide out-of-distribution or incorrect outputs.
With Wiz AI-SPM, you’d immediately gain visibility into which datasets were compromised via the AI-BOM functionality. The risk assessment tool would identify the malicious patterns in the training data, while proactive mitigation steps would recommend retraining the model with clean data and additional adversarial defenses to prevent future attacks.
Ready to learn more? Read more about Wiz AI SPM, or if you prefer a live demo, we would love to connect with you.
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