There should be a protocol in place for the introduction and removal of potential tools and services from the corporate inventory, while also maintaining a comprehensive record of the current equipment in use.
Training on Artificial Security and Privacy
The common notion that “humans are the weakest link” can be reshaped by effectively incorporating artificial security and privacy training into the process of integrating generative AI and LLM technologies within an organization.
This training is designed to enlighten employees about the ongoing generative AI/LLM initiatives, the overall functionalities of the system, and critical security aspects like data leakage. Building a culture of trust and transparency is crucial to foster an environment where employees feel comfortable sharing information about the generative AI and LLM tools and services being employed.
Establishing trust and transparency within the organization is vital to prevent unauthorized or unethical use of AI, as individuals may be reluctant to report issues to IT and Security teams due to fears of potential repercussions.
Development of Use Cases for AI Implementation
Surprisingly, many businesses overlook the importance of creating coherent strategic business cases for leveraging advanced technologies such as generative AI and LLM, unlike the strategic approach taken with cloud technology in the past. It is easy to get carried away by the excitement and competitiveness without a solid business rationale, leading to subpar outcomes, increased risks, and unclear objectives.
Governance Framework
Effective governance plays a pivotal role in clearly defining responsibilities and objectives. This section entails creating an AI RACI map to delineate the roles and responsibilities for managing risks and establishing organization-wide AI policies and procedures.
Legal Considerations
The legal implications of AI should not be underestimated and necessitate input from legal experts. These implications are rapidly evolving and can have significant financial and legal implications for the business. Activities in this realm include addressing solution warranties, AI End-User License Agreements (EULAs), ownership rights for scripts generated with AI tools, internet-related risks, and agreement indemnification clauses.
Regulatory Compliance
Apart from legal considerations, organizations must stay informed about evolving regulations such as the EU’s AI Act and other upcoming laws. Understanding and adhering to AI regulations at various levels—national, state, and local—is imperative. Organizations should also be cognizant of their AI vendors’ data management practices and their compliance with relevant laws.
Implementation of LLM Solutions
The implementation of LLM solutions requires a comprehensive risk assessment and control measures. Tasks include identifying vulnerabilities in LLM models and supply chains, ensuring pipeline security, mapping data workflows, and implementing access controls. Regular third-party audits, penetration testing, and code reviews are also recommended for suppliers.
Testing, Evaluation, Verification, and Validation (TEVV)
Following NIST guidelines, organizations should establish continuous TEVV processes throughout the AI model lifecycle. This involves ongoing testing, evaluation, verification, and validation to offer executive insights into the functionality, security, and reliability of AI models.
Model and Risk Cards
The utilization of model and risk cards can aid in promoting the ethical use of LLMs, fostering user trust, and facilitating open discussions about potential biases and privacy risks. These cards should encompass details on model architecture, training data methods, and performance metrics, while also addressing ethical concerns related to fairness and transparency.
RAG: LLM Optimization
Implementing Retrieval-Augmented Generation (RAG) techniques can enhance the capabilities of LLMs in extracting relevant data from specific sources. By optimizing pre-trained models or re-training existing ones with new data, organizations can maximize the value and utility of LLMs for their operational needs.
AI Red Teaming
The checklist recommends employing AI red teaming to simulate adversarial AI attacks, testing existing controls and defenses. While red teaming is valuable for ensuring secure generative AI and LLM adoption, it is crucial to acknowledge its limitations and the necessity of evaluating external generative AI and LLM vendors’ systems and services to prevent policy breaches and legal repercussions.
