Organizations today are collecting and storing ever-increasing amounts of sensitive information in multiple different computing environments. The explosion of cloud computing over the past decade led massive data sprawl, where vast amounts of information were scattered across disparate systems. Now, a new challenge has emerged: Generative AI (GenAI). While it is a groundbreaking technology with immense potential, GenAI has further exacerbated the data management problem.
Without a robust data governance strategy, GenAI’s potential can be overshadowed by privacy concerns, security vulnerabilities, and a lack of control over your organization’s data. This article explores the specific risks associated with GenAI and provides a starting point for establishing effective data governance practices to harness the power of GenAI while safeguarding your valuable data assets.
What is data governance?
Data governance helps organizations manage their data effectively. It encompasses a set of policies, procedures, and technologies designed to ensure data confidentiality, integrity, and availability. Effective data governance protects your organization from security breaches, compliance violations, and ultimately, a loss of customer trust.
Best Practices for Data Governance Excellence
Before we dive into good data governance practices for organizations using GenAI, here are some basic data management principles to keep in mind for data stored on-prem and in the cloud.
On-Premises Data:
- Classify Your Data: Not all data is created equal. Classify your on-premises data based on sensitivity (think financial data, employee records, usernames and passwords, PII, SPII) to determine appropriate security measures.
- Policies for Data Management: Define clear policies for data handling, retention, and disposal on-premises. This helps maintain data integrity, avoid data sprawl, and ensure compliance with regulations.
- Data Location and Jurisdiction: While location may be less of a concern for on-premises data, consider any jurisdictional regulations that apply to the specific data type.
- Ownership and Responsibility: Clearly define data ownership and appoint custodians responsible for data security and access control on your internal systems.
- Security First: Put robust security controls in place. This includes encryption, access control lists (ACLs), and firewalls to safeguard your on-premises data.
Cloud Data:
- Classification is Key: Classification becomes even more important in the cloud due to shared responsibility models with cloud providers. Classify data to leverage cloud provider security features effectively.
- Cloud-Specific Policies: Develop data management policies that address cloud-specific concerns like data residency, data transfer restrictions, and backup/recovery procedures.
- Location Matters in the Cloud: Data location in the cloud can be critical. Policies should address compliance with regulations like GDPR or CCPA depending on data type and storage location.
- Least Privilege Access: Leverage the cloud provider’s Identity and Access Management (IAM) tools and permission settings to grant least privilege access to cloud data based on user roles and needs. Conduct regular access reviews.
- Cloud Security Arsenal: Learn about and use the cloud provider’s security features like encryption at rest and in transit, cloud firewalls, and intrusion detection systems (IDS) to secure your data.
Generative AI: Data Governance in a New Age
Generative AI services hold immense potential for enhancing productivity and enabling operational excellence in organizations, but their reliance on personal data raises significant privacy and security concerns.
Understanding the Risks
Generative AI models are trained on massive datasets. Once personal data becomes part of this training set, it influences the model’s outputs. This can lead to:
- Privacy Breaches: Sensitive data like health information, financial data, PII and SPII might be extracted and republished, potentially leading to identity theft or fraud.
- Data Amplification: Generative AI can create massive amounts of data similar to the original input. This data can be misused by third parties for malicious purposes like phishing scams or invasive advertising.
- Loss of Control: Once data is shared with generative AI, tracking and managing its usage becomes extremely difficult. Retracting such data may be close to impossible.
- Deepfakes: Generative AI can be used to create hyperrealistic but entirely false content, like videos or audio recordings of real people. These deepfakes can be used for disinformation campaigns, harassment, or fraud.
- Biased Output: The accuracy and fairness of generative AI output heavily depend on the quality and diversity of the training data. If this data is biased, the AI’s outputs can also be biased, leading to discriminatory outcomes.
Jailbreaks and Prompt Injection Attacks
These attacks exploit vulnerabilities in generative AI models to bypass safeguards and manipulate output. Jailbreaks involve crafting prompts that trick the AI into generating hateful content, illegal activities, or bypassing security measures. Prompt injection attacks aim to insert malicious data or instructions into the model, potentially leading to unauthorized access, manipulated responses, or data theft.
The Importance of Safeguards
As generative AI becomes more widespread, robust safeguards are critical to mitigate associated risks. Here are some key strategies:
- Strong Data Protection Controls: Become familiar with and apply data minimization principles, user consent for data usage, and secure data storage practices.
- Ethical AI Practices: Develop clear ethical guidelines for responsible AI development, deployment and usage.
- Robust Legal Protections: Use legal frameworks to help ensure data protection and responsible AI development.
Like with on-prem and cloud data, the following data governance steps remain important with GenAI-accessed and GenAI-generated data.
- Classifying the New and Old: Classify both the data used to train generative AI models and the data they generate based on sensitivity and potential for bias.
- AI Policy Development: Develop specific policies for managing data shared with GenAI, data accessed by in-house GenAI solutions, and data generated by GenAI platforms including ownership, usage and sharing restrictions, regulatory compliance, potential privacy risks and how to address these.
- Controls Around Training Training Data: Implement security controls to protect training data used for generative AI models and the generated data itself. Consider data anonymization techniques.
- Privacy Impact Assessments (PIAs): Conduct PIAs to identify and mitigate privacy risks associated with using generative AI and the data it processes.
- Contractual Safeguards: Carefully review and negotiate contracts with generative AI service providers to ensure data security, ownership, and compliance with relevant regulations.
- User Awareness and Training: Once you have appropriate policies in place, ensure everyone in the organization understands GenAI best practices and data security principles.
- Monitoring and Auditing: Continuously monitor generative AI outputs to detect and address any security or privacy issues.
The Role of Data Protection Executives
Data protection managers and executives can play a vital role in safeguarding personal data within organizations using generative AI. Here’s what they can do:
- Data Protection Impact Assessments (DPIAs): Conduct thorough DPIAs to assess the risks associated with generative AI and implement appropriate mitigation measures.
- Understanding AI-Specific Risks: Stay updated on the evolving technological risks associated with AI to effectively manage data protection.
- Collaboration with AI Taskforces: Work with dedicated AI taskforces within organizations can help establish responsible AI governance frameworks.
Building a Responsible GenAI Ecosystem
A multi-pronged approach is necessary to ensure the responsible development and deployment of generative AI. Here are some key considerations:
- Transparency and Accountability: Involving diverse stakeholders in the development process and ensuring transparency in AI decision-making is crucial.
- Third-Party Assessments: Regular assessments by independent bodies can help identify and address potential risks.
- Organizational Restructuring: Establishing AI taskforces with representatives from various departments (legal, compliance, data protection, IT security, communications) fosters a holistic approach to responsible AI governance.
By implementing these data governance best practices and tailoring them to your specific data environment, you can ensure the security, privacy, and compliance of your sensitive data, both on-premises and in the cloud. Remember, data governance is an ongoing process. With GenAI especially, it is important to regularly review and update your practices to keep your data secure.
How CYRISMA can help:
CYRISMA’s multi-feature cyber risk management platform includes powerful data discovery, classification and risk mitigation capabilities. You can leverage the Platform to:
- Find and classify sensitive data in both your on-prem and cloud computing environments (Microsoft 365, Google Workspace).
- Secure sensitive data by encrypting, deleting, or moving it to a secure location, or by modifying access permissions.
- Perform a Microsoft Copilot Readiness Assessment to ensure that you continue to meet stringent security and data governance standards as you leverage the transformational capabilities of Microsoft Copilot.
- Track data privacy compliance with multiple frameworks like HIPAA, PCI DSS, SOC 2, NIST CSF, the CIS Critical Controls, the Essential Eight, and the UK’s Cyber Essentials
- Get estimates of the monetary value of your data on the dark web and potential losses in case of a ransomware attack or data breach.