In today’s rapidly evolving business landscape, more and more mid-sized and large enterprises are deploying, or getting closer to deploying, large language models (LLMs). These sophisticated generative artificial intelligence (GenAI) systems offer countless advantages, from automating repetitive, complex, or just time-consuming tasks to enhancing customer experiences. However, their adoption is not without its challenges, present and future.
The most immediate challenge is how to balance AI security with organizational enablement in such a way that the AI security team, Compliance, Legal, and Operations are all satisfied that their goals will be met. We consider a few of the issues that all teams would be concerned about, although from differing perspectives.
Data Privacy and Compliance
Challenge: Ensuring that LLMs operate within the boundaries of existing and future data privacy regulations, such as the EU’s General Data Protection Regulation (GDPR) and the California Consumer Protection Act (CCPA), as well as internal privacy practices around intellectual property (IP), personally identifiable information (PII), and other types of confidential or proprietary data
Solution: Employ robust solutions that ensure sensitive content is gathered, stored, handled, and shared in accordance with applicable regulations and internal policies; these could include data anonymization and encryption techniques, strict access controls, auditing mechanisms, and traceability and attribution tools
Challenge: Ensuring rules and policies are created, shared, explained, and followed
Solution: Establish a cross-functional team that includes stakeholders and users to create AI policies that are based on company principles and align to company values; ensure the policies identify responsible AI stewards, define acceptable uses, and include internal controls, including auditing model usage and oversight
Challenge: Guarding against misuse of LLMs by employees or insiders
Solution: Create a culture of security awareness across the organization through comprehensive and continual employee training, but don’t rely on people to follow through: implement strict role- or policy-based access controls, set prompt review filters to a high sensitivity, and use behavioral analytics to help detect and prevent insider threats
Scalability and Performance
Challenge: Ensuring LLMs perform efficiently during scale-up without compromising security
Solution: Optimize infrastructure by first understanding which tools are on the system, how well integrated they are, and what they contribute to the mix; monitor model usage during scale-up, conduct regular performance assessments to gain insight into functionality, relevance, and utility, and establish a cadence for determining when models might need to be retrained, retired, or replaced
Resource Allocation/Cost Management
Challenge: Effectively allocating resources to manage AI security and enablement initiatives and realize return on investment (ROI)
Solution: Prioritize investments in security tools specifically built to protect AI components, provide ongoing personnel training and education, and create and enforce AI governance structures that align with the organization’s goals and risk tolerance
Deploying and maintaining LLMs across an enterprise involves navigating a range of challenges; by addressing these challenges proactively with proven solutions, such as Moderator, organizations can harness the power of AI while maintaining the highest standards of security and compliance. Moderator is CalypsoAI’s model-agnostic “weightless” trust layer that resides between your organization’s digital infrastructure and public models to provide 360° protection without introducing latency.
A broad set of customizable scanners review every prompt and response to ensure that private information does not leave the system and malicious content, for example, embedded code or links, does not enter. Policy-based access controls can be applied to individuals, teams, and the models, so only personnel who need access to a model have it, and rate limits can be set to monitor cost and/or preclude Model Denial of Service (DoS) and similar attacks.
Because it is external to the system, Moderator affords the security team full observability into every AI tool in use, enabling real-time detection of anomalous activity that could indicate a threat or attack. Every interaction with every model, and every administrator interaction with Moderator, is tracked for review and auditing, based on administrator preferences. Admins can decide not to retain interactions, retain them indefinitely, or purge the information manually or automatically on a self-set cadence. Detailed insights about user behavior and model usage are available via a clear, interactive dashboard.
Close and ongoing collaboration among stakeholders across business functions is essential to successfully striking the right balance between AI security and organizational enablement and to achieving the full potential LLMs can provide in today’s business ecosystem. Adding Moderator to the AI security apparatus ensures your GenAI deployments are and remain transparent, secure, and stable.
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