AI Compliance with Operational Reliance
- Leah Pollack
- 20 minutes ago
- 2 min read
Artificial Intelligence “AI” is an umbrella term that encompasses a variety of technologies, solutions, and capabilities, such as Machine Learning (ML), Large Language Models (LLMs), and Generative AI (GenAI), and it is evolving the ways of working for all business and technical operations.
Customized Governance Frameworks
With a solid governance framework, your AI systems will operate ethically and comply with all the necessary regulations. This reduces legal risks and boosts the ethical integrity of your AI projects.
Consider including the following:
Policy and Process Design: Develop detailed policies based on top industry standards like those from the Responsible AI Institute and NIST. These policies guide the development, deployment, and monitoring of your AI systems, making sure they align with your goals and ethical standards.
Risk Management Strategies: Take a proactive approach to keep your organization safe from potential pitfalls and manage risks like bias, privacy issues, and security threats. By identifying and mitigating these potential risks, your framework enhances the reliability and safety of your AI systems, protecting your organization from unexpected challenges.
Compliance Assurance: Detail data protection regulations and ethical guidelines, so your AI initiatives are always legally sound and trustworthy. A well-governed AI system builds trust among your stakeholders, including customers, employees, and regulators. Transparent and accountable AI practices lead to greater acceptance and support.
What can you do to level up the DevOps in the AI ecosystem, making sure your AI is both legally sound and dependable in its performance?
Join the conversation and share your insights in the comments section below.
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