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The “trust layer” in an enterprise that uses generative artificial intelligence (GenAI) models and large language models (LLMs) is a concept both simple and complex at the same time. It is simple because it is the organization’s protective shield, crafted from established protocols and mechanisms that safeguard systems and processes while enabling trust and confidence in all user interactions with the model. It’s complex because it must encompass every model across the organization and touch every business function the models support, and its success depends on the engagement of every user. 

The Risk of Not Having a Trust Layer

While a trust layer is a somewhat new development in the AI security domain, its importance, if not criticality, within an organization that uses AI models cannot be overstated. The risks an organization faces when it doesn’t have an embedded trust layer include the following:  

  • Resource Allocation: The organization can incur significant ongoing and increasing costs in terms of money, time, and talent if it must implement, manage, and maintain in-house the security and privacy protections required by and for individual models.  
  • Data Privacy: Employee training and education by themselves are of limited utility when attempting to keep private, confidential, proprietary, or otherwise sensitive data from being shared outside the company. A distracted, disinterested, or disgruntled employee could include such information in a prompt and, without any built-in, automated review features, no one else would know the information had been shared with a third-party. 
  • Security: In a multi-model environment operating at scale, managing access can quickly become an exercise in frustration for the users and chaos for the AI security team as groups and individuals require access to different models or sets of models. The inclusion of internal, fine-tuned models trained on confidential data, such as financial or payroll records or source code, can exacerbate the risk of someone inadvertently getting access to information they should not see. 
  • Poor/Non-Existent Transparency: As noted above, managing and maintaining an array of models in use across the enterprise is a big job for any AI security team. Managing them as a single set of transparent models is the ideal, but the reality is that many current scenarios require manual oversight of a diverse set of siloed models that operate in parallel. In addition to being cumbersome and inefficient, this structure serves as a force-multiplier for risk exposure as unseen issues, non-compliant use cases, and other situations can occur unbeknownst to the security team until a manual audit is conducted.  
  • Compliance: Ensuring both model performance and human behavior conform to required company policies, industry standards, and government regulations is a crucial component of establishing confidence in the models. The consequences of failure to comply can range from legal action, loss of consumer and stakeholder trust, and damage to the brand, to, in the case of some regulatory structures, highly punitive fines.                                                                              

Benefits of Establishing a Trust Layer

The inclusion of a trust layer in or “above” the AI security infrastructure, such as CalypsoAI’s LLM security and enablement solution, is a game-changer in many respects. At the most basic, human level, everyone touched by the AI security task, from the third-shift tech support engineer to the chief security officer, will enjoy some peace of mind that precautions are in place to defend and protect the company’s intellectual property from known threats. At the tech level, automated processes provide insight into human, model, and system activity, and can identify anomalies and trends exponentially faster than a human can. The benefits of incorporating a comprehensive trust layer, such as CalypsoAI, into the organization include the following:

  • Proactive Risk Management: Establishing a set of both technical and human controls, including policies and other AI governance mechanisms, provides a framework for anticipating, responding to, and recovering from security incidents. For instance, continuously monitoring model traffic means external threats and incursions can be identified and neutralized according to an in-house rapid response plan. 
  • Data Privacy Protection: Automated, customizable scanners are crucial solutions for safeguarding data. These tools review user prompts to ensure proprietary or sensitive information, administrator-identified terms, secrets, such as API keys, and personally identifiable (PII) employee or customer information are not included in the content. If such data is found, the scanners intercept the prompt and either block it from being sent or redact the prohibited content to protect system integrity.   
  • Enhanced Security: Policy-based access controls that enable administrators to limit LLM permissions to specific groups or individuals provide a layer of security beyond standard multi-factor identification and other identity and access management protocols. Applying rate limits and other managed rules can enhance protections against model overuse by authorized users and help prevent Model Denial of Service attacks.  
  • Transparency Tools: Full observability across the model array enables understanding of the AI security infrastructure’s threat posture, as well as providing a high-level view of model usage. Continuous auditing, tracking, and monitoring features that retain every prompt and response for review and analysis allow detailed insights to be gleaned for each user and group.  
  • Appropriate Model Use: Automated, customizable response scanners ensure malicious code or content does not enter the system and administrator-defined bi-directional scanners review both prompts and responses for adherence to acceptable use policies and alignment to company values. Content such as toxic or banned terminology, PII, and legal documentation or language are blocked from leaving or entering the system. Additional auditing scanners provide insight into prompts containing non-business topics, named entities, or specific demographic information, as well as indications of user sentiment, where allowed by law.  
  • Compliance: As national, international, and industry regulatory bodies continue to craft more and more legislation and guidelines for data processing, transfer, and use, the need to comply continues to grow in  importance. Our solution’s customizable scanner, use, and permissioning settings enable administrators to adapt quickly to policy, legislative, or other changes.

 Implementing a Trust Layer 

Building a strong trust layer is a multi-faceted, ideally cross-functional endeavor that both involves and addresses the entire organization. Establishing such a structure begins with a governance framework that identifies vulnerabilities and the controls that will contain them, creates and implements policies to support the controls, and develops a rapid response and recovery plan that remains a living document and is updated as the threat environment evolves. The framework must include employee education and training as key elements to ensure enterprise-wide user awareness of both authorized activity and threat attempts, such as increasingly sophisticated phishing emails, social engineering, and malicious content. Close and ongoing collaboration between the cyber, IT,  and AI security teams as the trust layer is deployed is critical to ensure total situational awareness of the entire attack surface. 

Embedding a trust layer must move from being a “nice-to-have” feature to being a high priority for cyber, IT, and AI security professionals deploying LLMs and GenAI models across the enterprise. Doing so ensures the responsible, secure, and accountable use of these technologies, and significantly mitigates the risks and ethical concerns associated with their widespread adoption, while maintaining transparency and accountability in their operations.