Organizations are rushing to capitalize on the artificial intelligence (AI) trend, however, often the proper controls and planning are not in place, resulting in significant risks. Key to successful generative AI deployments are clear guardrails and guidelines for training the AI model, along with proper controls and metrics to track progress and cite issues.

AI governance is an offshoot of IG, and includes a comprehensive framework of principles, policies, regulations, and operational practices that steer the entire lifecycle of AI technologies, from development to deployment to usage. This multifaceted approach addresses a wide array of vital factors, such as ethics, accountability, transparency, fairness, privacy, and security. The significance of AI governance lies in its capacity to guarantee the responsible and value-aligned evolution and application of AI technologies, serving the betterment of society as a whole. In the absence of robust governance, AI has the potential to amplify preexisting disparities, compromise individual rights, and introduce risks that may impact both individuals and the broader community.

Without proper AI governance controls and “guardrails” AI program deployments can suffer from:
Bias and Discrimination: Without proper governance, AI systems can perpetuate and even amplify biases present in training data, leading to discriminatory outcomes. This can have serious ethical and legal implications and harm marginalized communities.

Privacy Violations: AI systems might inadvertently infringe on individuals' privacy by collecting, processing, or sharing personal data without consent or inappropriately.

Security Risks: Ungoverned AI deployments can become targets for cyberattacks. If AI systems are not adequately secured, they can be exploited, leading to data breaches, unauthorized access, or other security breaches.

Reputation Damage: Poorly managed AI deployments can result in algorithmic errors or unintended consequences, which can tarnish a company's reputation and trustworthiness, and reduce its brand equity value.

Regulatory Compliance Issues: Failing to adhere to data protection and AI-related regulations can lead to legal issues, fines, and penalties, which can be costly for businesses.

Loss of Accountability: Unclear responsibility for AI system outcomes can lead to a lack of accountability, making it challenging to rectify issues and assign responsibility when things go wrong.

Ineffective or Inefficient Operations: Ungoverned AI might not operate optimally, leading to inefficiencies or errors in business processes.

Missed Opportunities: Without proper governance, organizations might underutilize AI's potential, missing out on opportunities for automation, data-driven insights, and improved decision-making.

Ethical Concerns: AI deployment without ethical guidelines can raise moral questions about the technology's use in various applications, such as surveillance, autonomous weapons, or job displacement.

Lack of Transparency: Without governance, AI models might become "black boxes," making it difficult to understand their decisions and leading to a lack of transparency and trust. To mitigate these dangers, it is crucial for organizations to establish robust AI governance frameworks that include clear ethical guidelines, rigorous data handling procedures, security measures, and compliance with relevant laws and regulations. These measures must necessarily be far more rigorous, advanced, and comprehensive than prior attempts at IG, if only because AI is so much more powerful than earlier information technologies. Additionally, fostering a culture of responsibility and accountability within the organization is essential for safe and effective AI deployment.