Align with NIST AI Risk Management Framework with Lumeus
AI Access Control,AI Guardrails,Enterprise AI,GenAI,LLM,Shadow AI
ByRobertsonDecember 16, 2024
As artificial intelligence (AI) continues to transform industries, the importance of implementing robust governance frameworks for AI systems becomes increasingly clear. With the rise of private AI applications—such as generative AI models, chatbots, and AI-driven decision-making tools—the risks associated with improper use or deployment of these technologies have grown significantly. To address these risks and ensure responsible AI usage, organizations need strong AI guardrails in place.The NIST AI Risk Management Framework (AI RMF) offers a comprehensive approach to AI governance, focusing on risk management, transparency, accountability, and compliance. By integrating these principles, Lumeus provides a powerful solution for organizations looking to implement effective guardrails for both private and public AI systems. Let’s explore how Lumeus aligns with the AI RMF and enables businesses to secure their AI-driven environments.1. Visibility: Monitoring AI Usage with AccountabilityOne of the core components of the NIST AI RMF is “Govern,” which emphasizes the need for transparency and accountability in AI systems. This is where Lumeus shines—by providing organizations with real-time visibility into who is using AI tools, such as chatbots or other generative AI applications.Lumeus helps businesses monitor the users interacting with these systems, allowing administrators to understand how and by whom the AI is being accessed. This visibility is critical for maintaining control over AI usage, ensuring that unauthorized or risky access is flagged and addressed. By keeping track of interactions, Lumeus supports organizations in creating a transparent AI usage framework, fulfilling one of the primary objectives of AI governance.2. Automatic Classification: AI-Driven Topic DetectionEffective governance of AI systems requires the ability to measure and assess risks in real-time. This is achieved through Lumeus’ automatic classification of AI interactions, which leverages sophisticated topic detection technology. Every time a user engages with an AI application, Lumeus automatically categorizes the interaction, analyzing the content and context to ensure compliance with organizational policies.This aligns with the “Map” and “Measure” functions of the NIST AI RMF, which focus on identifying AI risks and monitoring AI performance. Through automatic classification, Lumeus helps businesses measure the impact of each AI interaction, enabling them to understand the nature of the conversations or tasks being handled. It provides actionable insights into potential biases, inaccuracies, or inappropriate outputs, which can then be addressed promptly.3. Topic-Based Access Control: Managing AI Interactions with PrecisionOne of the most critical aspects of AI governance is ensuring that sensitive data and high-risk topics are handled appropriately. This is where Lumeus’ topic-based access control system proves invaluable. Lumeus allows organizations to define specific topics or types of interactions that need to be monitored or restricted, offering a highly customizable security mechanism for AI tools.By applying topic-based access control, organizations can block or monitor interactions that involve sensitive or regulated content. For instance, if a conversation in a chatbot touches on financial data, legal matters, or health-related topics, Lumeus can enforce appropriate restrictions, ensuring that only authorized individuals or systems can access these sensitive areas. This capability strengthens security and ensures compliance with privacy regulations, aligning with the “Manage” function of the NIST AI RMF.Moreover, topic-based access control helps prevent the spread of biased or misleading information by ensuring that certain subjects are closely monitored or entirely denied, protecting both the organization and end users.4. AI Governance: Lumeus and NIST RMF AlignmentBy incorporating the AI RMF principles into its architecture, Lumeus offers a comprehensive AI governance solution that helps organizations mitigate the risks of AI deployment. Here’s how Lumeus’ features align with the core functions of the NIST AI RMF:Govern: Lumeus provides visibility into AI usage, enabling organizations to hold users accountable and ensure AI tools are being accessed appropriately.Map: Through automatic classification and topic detection, Lumeus helps organizations assess and map the potential risks of AI interactions in real time.Measure: Lumeus offers insights into AI performance, enabling organizations to measure whether AI tools are meeting compliance and ethical standards.Manage: Lumeus’ topic-based access control allows businesses to manage AI interactions with precision, ensuring that sensitive or high-risk topics are appropriately handled. Conclusion: Strengthening Guardrails for a Secure AI FutureAs AI technologies evolve, the need for effective governance and security becomes more pressing. Lumeus offers a sophisticated solution for implementing AI guardrails, enabling organizations to deploy private and public AI systems with confidence. By integrating visibility, classification, and access control into one seamless platform, Lumeus helps businesses safeguard their AI environments, comply with regulatory frameworks, and mitigate risks.Whether you are deploying AI-powered chatbots, recommendation systems, or other generative AI applications, Lumeus empowers you to build a robust, secure AI ecosystem with the right guardrails in place. With Lumeus, organizations can ensure that their AI systems are used responsibly, ethically, and securely—meeting the demands of the modern AI landscape.Demohttps://www.youtube.com/embed/X9FyyOPDR9Y

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AI Guardrails: Securing the Future of Generative AI

AI Guardrails,Data Protection,GenAI,Zero Trust Security
ByAditya SoniApril 3, 2024
Summary

Generative AI has seen remarkable progress, transforming every industry. However, its integration comes with challenges and risks. Despite efforts to establish safeguards against these challenges, studies indicate that current measures may not fully prevent organizations against risks including issues related to privacy, bias, and ethics. The need for AI Guardrails is further underscored by incidents of inappropriate AI behavior and misinformation prompting organizations and government to emphasize robust AI governance.

What are AI Guardrails?

Gartner’s survey underscores Generative AI as a primary emerging risk, appearing in its top 10 for first time, the rapid adoption of Generative AI  (GenAI)  and Large Language Models (LLMs) raises privacy issues, with incidents of unintentional rule violations, accidental sharing of proprietary information, and unintended disclosure of confidential data for the sake of productivity.
AI Guardrails, or safeguards, refer to a set of policies, practices, and technologies designed to ensure the safe, ethical, and responsible use of GenAI and LLMs, within an organization. These measures are implemented to address and mitigate the risks associated with AI technologies, including privacy breaches, inherent biases, inaccuracies, and ethical concerns.
What are the types of AI Guardrails?

AI Guardrails can be categorized into several types, each designed to mitigate specific risks associated with the deployment and use of AI technologies. Here are some of the primary types:

Ethical Guardrails: Set limits to prevent biased or harmful outputs, ensuring GenAI output adheres to societal and moral standards.
Compliance Guardrails: Ensure outputs comply with legal standards, crucial in sectors like healthcare, finance, and law, focusing on data protection and privacy.
Contextual Guardrails: Adjust GenAI to produce content appropriate for specific situations, avoiding potentially inappropriate but legal outputs.
Security Guardrails: Protect against security risks, preventing misuse that could lead to data breaches or spread of misinformation.
Adaptive Guardrails: Enable guardrails to evolve, maintaining ethical and legal integrity as models learn and adapt over time.

Are the current AI Guardrails sturdy enough?

According to an article in The New York Times, before the release of the AI chatbot ChatGPT, OpenAI, put in place digital precautions to prevent the creation of hate speech and disinformation by the system. Google followed a similar approach with its own Bard chatbot.
However, a study conducted by researchers from Princeton, Virginia Tech, Stanford, and IBM indicates that these safeguards may not be as robust as AI organizations believe.
This research underscores the growing concern that, despite efforts by companies to mitigate AI misuse, potential for generating harmful content remains. The complexity inherent in the technology driving these advanced chatbots means that as their functionalities expand, controlling their actions becomes increasingly challenging.
Important questions emerge for IT/Security teams:

How can the accuracy of the outputs be confirmed?
What steps are taken to ensure outputs are legally compliant?
How can we guarantee the system’s outputs are safe for users?
What measures are in place to reduce bias?

Why do we need AI Guardrails?

Upon the release of ChatGPT-3.5 by OpenAI in November 2022, there was significant public interest. Microsoft’s announcement in February 2023 about integrating similar AI functionality into Bing resulted in over 1 million people signing up to test it within two days and not long after, as testers began to use the GenAI models, strange results started showing up, including an incident where Bing, revealing a persona named Sydney, expressed disturbing thoughts and attempted to disrupt a journalist’s marriage.
Concurrently, Google unveiled its GenAI model, Bard, which mistakenly provided incorrect information during a demonstration, leading to a significant financial loss for Google’s parent company, Alphabet, due to a drop in share price. 
During a session titled “The Transformative Power of Artificial Intelligence” at a NACo Legislative Conference, panelists emphasized that artificial intelligence (AI) is “captivating, disruptive, and transformative,” representing a pivotal tool for county-level progress. However, they stressed the importance of intergovernmental cooperation in establishing safeguards to mitigate AI’s risks.
In parallel, the Biden administration has issued new guidelines for federal agencies on appropriate AI usage. This move marks a significant effort towards safeguarding GenAI.
Additionally, Meta has recently committed to enhancing AI Guardrails, aligning with global initiatives by governments to create a robust regulatory framework for GenAI. This effort seeks not only to set boundaries for AI’s application but also to lay the groundwork for its trusted integration into society.

 
In the absence of appropriate safeguards, GenAI poses several risks, including:

Data Privacy: Businesses hold sensitive information that necessitates robust guardrails to avert misuse by AI.
Regulatory Compliance: With stringent legal frameworks in place, ensuring that AI complies with both local and global regulations is paramount.
Reputation Management: Inaccuracies or ethical missteps in AI applications can tarnish a company’s image. Implementing guardrails helps mitigate such risks.
Ethical Integrity: Public concern around AI underscores the need for guardrails that confine AI’s application to contexts that align with human ethical standards.

 
Lumeus.ai offers Zero Trust Security for AI, enabling IT Security to efficiently manage ShadowAI, control AI access, and enforce AI Guardrails. It integrates seamlessly with existing security infrastructures, supporting identity platforms like Okta, Google, Active Directory, and network security platforms from Palo Alto, ZScaler, Fortinet, enabling a smooth deployment.
 
If you’re interested in a deeper discussion or even in contributing to refining this perspective, feel free to reach out to us.

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