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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Shadow AI, Navigating the Shadows in GenAI

AI Firewall,Data Protection,Access Management,Shadow AI,Top 5 LLM Security Risks
ByAditya SoniMarch 22, 2024
Summary

In the rapidly evolving digital landscape, Shadow AI has emerged as a silent disruptor, posing both challenges and opportunities for organizations across the globe. As departments outside of traditional IT channels increasingly deploy AI solutions to enhance efficiency and decision-making, the risks associated with these unsanctioned initiatives become more pronounced. This comprehensive guide explores the concept of Shadow AI, uncovers the multifaceted risks it presents, and lays out a detailed blueprint for organizations seeking to harness the benefits of AI while mitigating its inherent risks.

What is Shadow AI?

Over the last twenty years, businesses have faced the hurdles of employees bringing their own devices and using their personal technology at work, a phenomenon known as shadow IT. Now, companies are dealing with a new trend in artificial intelligence. This involves employees using AI tools meant for general consumers in professional settings, a practice we’re referring to as Shadow AI.Shadow AI refers to the development and utilization of artificial intelligence applications within an organization without explicit oversight or approval from central IT.

What is driving Shadow AI?

Several factors contribute to the rise of Shadow AI within organizations:

Rapid Technological Advancement: The pace of technological innovation encourages departments to quickly adopt new AI tools to gain a competitive edge.
IT Bottlenecks: When IT departments are overwhelmed or slow to respond, other departments might take matters into their own hands to avoid delays.
Lack of Awareness: There is often a gap in understanding the importance of compliance and security standards outside the IT department.

What are the risks and challenges of Shadow AI?

The unchecked growth of Shadow AI carries significant risks that can undermine the very benefits it seeks to provide:

Security and Privacy Concerns: Shadow AI applications may not be subject to rigorous security checks, increasing the risk of data breaches and privacy violations.
Regulatory Non-Compliance: Operating outside the oversight of IT governance, Shadow AI initiatives may fail to comply with industry regulations, exposing the organization to legal penalties.
Resource Fragmentation and Inefficiency: Duplicate efforts and incompatible systems can lead to resource wastage and operational inefficiencies.
Ethical Dilemmas: Without proper oversight, AI applications might be developed without considering ethical implications, leading to biased or discriminatory outcomes.

The challenges posed by Shadow AI are not only theoretical but have also manifested in significant real-world issues, as seen in the case of Samsung. The company was forced to ban the use of generative AI tools like ChatGPT.
Some Wall Street banks, including JPMorgan Chase & Co, Bank of America Corp, and Citigroup Inc, either banned or restricted the use of ChatGPT, these banks recognized the potential security risks associated with the use of generative AI platforms and took proactive measures to prevent data leaks and protect their intellectual property.

What are the strategies to Manage Shadow AI?

Leveraging Technology to Centralize AI Management: AI management platforms and tools can provide a centralized overview of all AI applications within the organization, allowing for better control and management. These tools can help in:

Monitoring AI Applications: Identify and assess all existing AI tools and projects across the organization.
Assessing Risks: Evaluate the security, compliance, and ethical implications of AI applications.

Building an AI Governance Framework: An AI governance framework establishes the rules of engagement for AI projects, detailing the processes for approval, development, deployment, and monitoring. This framework should:

Define AI Ethics and Principles: Set clear ethical guidelines for AI development and use within the organization.
Establish Approval Processes: Implement a streamlined process for departments to propose and gain approval for AI projects.
Set Security and Compliance Standards: Outline mandatory security protocols and compliance checks for all AI applications.

Cultivating a Culture of Transparency and Collaboration: A culture that promotes open dialogue and collaboration between IT and other departments can significantly reduce the appeal of pursuing Shadow AI initiatives. Encouraging departments to share their technological needs and challenges can foster a more cooperative approach to AI development, ensuring that projects are aligned with organizational standards and goals.

Educating Stakeholders on the Importance of Governance: Ongoing education and training for all stakeholders involved in AI development are crucial. Workshops, seminars, and resources on the importance of security, compliance, and ethical considerations in AI can raise awareness and foster a more responsible approach to AI projects.
Implementing Continuous Monitoring and Evaluation: Regular audits and reviews of AI projects can ensure they remain compliant with organizational policies and regulations. This continuous monitoring process helps identify potential issues early, allowing for timely interventions to mitigate risks.

Conclusion

Shadow AI shows us both sides of tech innovation – it brings great benefits but also new problems. By getting to grips with Shadow AI and having a solid plan to handle it, companies can use AI to spark new ideas and work smarter, without the downsides.
Moving from hidden risks to clear benefits doesn’t mean stopping innovation. It means guiding it with good management, teamwork, and doing the right thing. This way, companies can use AI as a strong force for moving forward, making sure it’s safe, follows the rules, and is fair to everyone.
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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Shadow AI and its threat on GenAI and data protection

AI Firewall,Data Protection,Access Management,Shadow AI,Top 5 LLM Security Risks
ByMattMarch 4, 2024
Summary

Surveys reveal that while 76% of IT leaders predict a transformative role for Generative AI in their companies, with 65% expecting benefits within a year, there’s a growing trend of employees using GenAI tools without official authorization.
Samsung banned generative AI tools like ChatGPT due to leaks of confidential information, highlighting ongoing concerns about AI security and privacy risks despite the evolving landscape.
Shadow AI poses greater risks than Shadow IT by exposing every level of an organization to potential data breaches, AI-generated inaccuracies, unauthorized access issues, and regulatory non-compliance.
Banning generative AI in the workplace can inadvertently increase risks by driving its use underground, bypassing security measures and highlighting the need for more nuanced management strategies.

What research from Dell, Salesforce and Forbes reveals about Shadow AI?

According to a recent Dell survey, 76% of IT leaders believe GenAI will play a crucial and potentially transformative role in their companies.
The same survey reveals 65% of IT leaders expect to see tangible benefits from GenAI within the next year.
A global Salesforce survey of over 14,000 employees in 14 nations found a significant number of generative AI (GenAI) users in the workplace are using these tools without official training, guidance, or authorization from their employers.
Forbes reports an increasing trend in the unauthorized use of generative AI within companies.

The rapid adoption of GenAI poses challenges, especially when employees use GenAI tools not officially sanctioned by the company. The trend of “shadow AI” usage heightens organizational risks, raising concerns around data security, regulatory compliance, and privacy.

Samsung’s Data Alarm: Shadow AI Emerges from the Shadows

The challenges of Shadow AI are significant, as evidenced by real-world incidents. Take for example the Samsung case :

Samsung banned use of generative AI tools like ChatGPT after they found that ChatGPT possessed confidential information.
The first incident was involving an engineer who pasted buggy source code from a semiconductor database into ChatGPT, with a prompt to the chatbot to fix the errors.
The second instance, an employee wanting to optimize code for identifying defects in certain Samsung equipment pasted that code into ChatGPT.
The third leak resulted when an employee asked ChatGPT to generate the minutes of an internal meeting at Samsung.

Gartner, as early as 2019, pinpointed security as a critical strategic trend in AI. Although the AI landscape has evolved since then, privacy risks remain a paramount concern, especially given the fast-paced changes in the AI field.

What is the impact and challenge of Shadow AI on your organization?

The impact of Shadow AI is expected to be greater than Shadow IT, as highlighted by cio.com. Unlike Shadow IT, where risks were mostly limited to developers, generative AI exposes every user in an organization, from admins to executives, to potential errors. From discussions with enterprise clients, several emerging challenges associated with shadow AI have been identified:

Data Protection: This includes the possibility of users inadvertently sharing confidential data with GenAI, leading to unintentional disclosure of sensitive information.
AI “Hallucinations”: This refers to instances where the AI chatbot generates inaccurate or misleading information, which can lead to incorrect decisions or misinterpretations.
Access Management: There’s a risk of the GenAI service provider gaining unauthorized access by employees. This could happen through human review of the customer’s data inputs to the AI system.
Non-compliance with Regulations: The use of unapproved GenAI tools can lead to breaches in cybersecurity and data privacy standards, failing to meet legal and regulatory compliance.

 
Will banning GenAI help organizations?

Banning Gen AI in workplaces might reduce visible risks but can drive its use underground, making it harder to control and  potentially increasing risks.
Covert use of Gen AI bypasses security controls and oversight, potentially escalating risks rather than mitigating them.
Outright bans can be counterproductive; more nuanced strategies are needed to manage Gen AI use effectively.

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.

Ready to see how Lumeus can streamline secure access to your private resources?
Get started instantly with the only LLM-Based Zero Trust Gateway
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