Building Trustworthy Generative AI Applications

In the magical landscape of Generative AI, opportunities come hand-in-hand with immense risks and responsibilities. Building truly Trustworthy AI is a continuous journey as we harness the revolution of Artificial Intelligence.?

Organizations face new challenges in defending and managing Gen AI solutions from the potential for accelerated threats from threat actors using augmented attack techniques. Business leaders, CISOs, and Legal Regulators must pay close attention and devise a strategy to benefit from unfolding possibilities while fighting against threats and managing risks.

Generative AIs' primary focus is creating content using language models trained on large textual data sets to understand, summarise, analyze, and generate new content.?LLM capabilities also introduce numerous attacks, such as prompt injection, insecure plugin design, remote code execution, bias and fairness concerns, data poisoning, adversarial attacks, data privacy, model inversion attacks, and the list is endless.??

Importance of Governance & Regulation:

Corporate governance is much needed to provide organizations with transparency and accountability. To achieve this, we must prepare an AI RACI chart to represent who is responsible, accountable, consulted, and informed. Define data management policies for classification, access level, and usage limitation to protect confidential data to input to the models for training. Organizations should maintain record books and metrics for input sources and output uses in and by the large language models.

Regulators are working diligently to seek traceability and design frameworks and policies for Trustworthy AI use (especially in Financial and Healthcare services). The European Union AI Act is anticipated to be the first comprehensive AI law but will likely apply in 2025. Many US states have passed laws that are already in action. A few other federal organizations, such as the US Equal Employment Opportunity Commission (EEOC), the Federal Trade Commission (FTC), and the US Department of Justice's Civil Rights Division (DOJ), are closely monitoring AI fairness.?

Costly Legal Implications:

The Legal Implications of Artificial Intelligence are costly and still need to be defined clearly. End-user license agreements for Gen AI applications are crucial in handling compliance, user prompts, output rights, data privacy, liability, use of output, etc. Code generation is one of the hot favorite use cases of Generative AI, but any source code the chatbot generates could raise ownership concerns for its product. Plagiarism, Trademarks, or any intellectual property generated by a chatbot could be in jeopardy if obtained data is used improperly during the generative process. It has become essential for businesses to have legal partnerships to identify gaps to review and update existing terms and conditions, ensure appropriate agreements with sub-contractors, and ensure the AI solutions collect, process, store, and share sensitive information only after proper consent and authorization.

Security and Review are Paramount:

As an AI service provider or any business adopting AI, it is paramount to determine country or state-specific laws and compliances. Review AI tools in use for employee hiring or management to avoid discrimination and bias. For applications such as facial recognition and AI video analysis, user consent is essential for compliance requirements.

-- Implement the least privilege access controls and classify personal and business data based on sensitivity.?

-- Evaluate input validation methods and how outputs are filtered, sanitized, returned, and consumed by the application.

-- Include defense-in-depth measures in application testing, vulnerability assessments, source code review, and red teaming in the production release process.

-- Perform impact analysis of attacks and threats to LLM models, such as prompt injections, model poisoning, improper data handling, and model theft.

-- Review infrastructure security, monitoring, scalability, logging, and performance, and define SLAs regarding availability.

Generative AI solutions present new risks and challenges, requiring unusual mitigating techniques. Pro-active awareness, on-time risk analysis, and strategic integration of controls, processes, and procedures allow businesses to reduce their vulnerability to threats.

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