Where Are AI Regulations Headed?
As businesses delve into the realm of artificial intelligence (AI), several pressing questions arise: What are the most significant dangers of AI? Will AI ever truly be trustworthy? How can organizations leverage advancements in automation while mitigating associated risks?
These inquiries are becoming increasingly common as AI systems, particularly Generative AI (GenAI), demonstrate real-world benefits and gain widespread adoption. The role of risk management is becoming critical in fostering innovation and maintaining trust.
The Current Regulatory Landscape
In the absence of formal legislation or regulation, companies are called to proactively establish appropriate risk and compliance guardrails. This includes implementing necessary “speedbumps” to navigate the evolving AI landscape.
This report highlights the current state of regulators and the trajectory they are on, shedding light on the risk issues that should be integrated into the design, development, deployment, and monitoring of “trustworthy” AI systems.
Understanding AI Risks
The benefits and risks associated with AI permeate various organizational layers, from operations and products to customer protections. Key areas of concern include:
- Privacy: Issues surrounding data collection, usage, protection, quality, ownership, storage, and retention.
- Data: Risks of data breaches, malware, fraud, identity theft, and other forms of financial crime.
- Security: Risks involving adversarial attacks, data poisoning, insider threats, and model reverse engineering. Rapid remediation is essential to manage reputational risks.
- Adoption and Integration: Operational risks related to AI adoption, including third-party risk management, overreliance on single providers, limited access to expertise, and the necessity of workforce training.
- Testing and Evaluation: Effective AI design necessitates robust testing, evaluation, verification, and validation processes throughout the AI lifecycle. Failures can impact intended use, user experience, and compliance with relevant requirements.
- Assurance and Attestation: Trustworthiness in AI systems is crucial for fostering a successful user experience, maintained through assurances that uphold confidentiality, integrity, and availability.
- Intellectual Property: Potential legal considerations surrounding intellectual property (IP) rights and the potential for devaluation.
Areas to Watch in AI Regulation
As regulatory attention shifts toward AI trustworthiness, several focal points emerge:
- AI Trustworthiness: Companies must reassess the purpose and application of AI, especially concerning data collection, inputs and outputs, and privacy and security measures.
- Business Risks: Existing policies and procedures may require reassessment in light of emerging public policies and regulatory actions regarding AI and associated topics like privacy and cybersecurity.
- AI Risk Management: Organizations must remain vigilant concerning the risks of misusing AI and the regulatory scrutiny that may ensue. An active AI risk management framework is essential to address these challenges.
Regulators will look for signs of robust AI development, effective independent validation of AI design, and sound governance, policies, and controls. As the landscape continues to evolve, businesses must stay proactive in adapting to these changes.
