- Policy-based strategies include:
- Establishing Data Governance Policies: Robust data governance policies should dictate how the organization categorizes, handles, shares, and protects data.
- Conducting Regular Audits: Regularly monitoring and auditing LLM outputs can help ensure compliance with data protection standards.
- Implementing Privacy-Preserving Techniques: Privacy-preserving techniques like data anonymization and differential privacy should be in place to protect sensitive information.
- Using Sanitized Training Data: The possibility of sensitive information being included and disclosed can be reduced by training LLMs with sanitized datasets.
- Incorporating Ethical Guidelines: AI development and deployment practices must be based on ethical decisions and protocols to ensure responsible use of models.
- Technical solutions to prevent oversharing include:
- Implement AI-Driven Monitoring Systems: AI-driven systems can monitor model outputs in real time, enabling issues to be identified and addressed before they become oversharing incidents.
- Integrate Encryption and Access Controls: Strict encryption mechanisms and role- or policy-based access controls will shield sensitive data from unauthorized access and disclosure.
- Review Case Studies: No better process exists than to learn from other organizations that successfully implemented oversharing prevention strategies.
Blog
08 Aug 2024
Mitigating Oversharing Risks in the Age of Large Language Models
Mitigating Oversharing Risks in the Age of Large Language Models
Mitigating Oversharing Risks in the Age of Large Language Models
The ability of large language models (LLMs) to generate increasingly conversant interactions across many diverse tasks has cemented their place in the business landscape. The introduction and enthusiastic acceptance of small, fine-tuned, retrieval-augmented generation (RAG) models and other focused models has brought those capabilities into the organizational perimeters, which means the models have access to company information. And that intersection of convenience and content is the epicenter of the risk of oversharing.
We define “oversharing” as a model disclosing more information than intended or necessary during a user engagement, such as customer interactions, automated content generation, and internal communications, which leads to sensitive data being exposed to unauthorized users. This inadvertent oversharing can have severe consequences, including data breaches, loss of proprietary information, and non-compliance with regulatory standards.
Effective mitigation requires a security audit to learn what vulnerabilities exist within your LLM deployments, as well as how to address them.
