A Streamlined Approach to Managing Regulatory Compliance for AI
As the adoption of artificial intelligence (AI) becomes increasingly pervasive across various business operations, addressing AI regulations and managing AI risk has become critical for organizations aiming to deploy AI with trust and confidence.
Navigating the complex landscape of regulations and compliance requirements can be daunting. Governments and regulatory bodies worldwide are scrutinizing AI deployments, and noncompliance can lead to significant reputational and financial risks. A streamlined approach to AI governance and compliance is essential as organizations struggle to keep pace with the growing complexity and volume of regulations and industry standards.
The Need to Streamline AI Compliance
Businesses increasingly rely on AI to improve productivity but may encounter instances where the technology behaves unexpectedly or makes mistakes. For instance, an AI recruiting tool may drift if its training data is biased or incomplete. In sectors like banking, housing, and healthcare, AI is utilized for critical tasks such as loan applications and treatment recommendations, each presenting unique challenges.
In response to these issues, a movement is emerging to standardize the development and usage of AI in the business landscape. The EU’s General Data Protection Regulation (GDPR) was a pioneering step, providing guidance on data privacy and governance, as well as the consequences of noncompliance. Following this, the EU AI Act introduces specific requirements for AI development and use within the European Union, representing just one of many evolving regulations globally.
Increased Risks and Costs of Compliance
Proactively managing AI compliance can help businesses avoid financial, legal, and reputational risks associated with AI usage. Noncompliance can result in severe penalties; for example, under the EU AI Act, companies may face fines up to EUR 35 million or 7% of their annual turnover in certain cases. Compliance also safeguards brand reputation, fostering consumer trust in data usage.
A 2024 survey by KPMG indicates that AI regulation may necessitate stricter data privacy and security measures, alongside increased costs. This underscores the importance of developing efficient compliance strategies to facilitate cost-effective AI growth and scaling.
Ever-Changing Regulations and Compliance Requirements
As AI technology rapidly advances—from predictive machine learning models to complex intelligent agents—regulatory compliance requirements and industry standards also evolve swiftly. Companies must be agile, responsive, and proactive in adapting to these developments.
The nature of generative AI complicates compliance activities. Understanding and interpreting AI models and algorithms can be technically challenging, especially as many AI systems operate in real time. Keeping pace with evolving regulations at this speed is demanding and necessitates that businesses continuously adapt their compliance programs.
The Growing Volume and Complexity of Compliance Regulations
Compliance officers must evaluate each AI business case against regulatory rules and policies. This is further complicated for large enterprises that must adhere to diverse geographical regulatory requirements in each operational area. Trying to manage these across various locations and use cases is extremely time-consuming.
Moreover, regulatory requirements can extend beyond local entities, applying to any business operating within a region, regardless of its headquarters or location.
Lack of a 360-Degree View of the Compliance Posture
Overseeing an organization’s compliance posture is challenging due to the dynamic nature of the regulatory landscape. Investing in multiple siloed tools for different use cases and regions often yields minimal return on investment (ROI).
Organizations require an enterprise-wide view of their compliance posture, coupled with automated compliance workflows that strengthen AI governance across various use cases within a single solution.
Scaling AI through an Efficient Compliance Process
To scale AI effectively and responsibly, the optimal approach involves using an end-to-end AI governance solution that supports efficient compliance capabilities and enforces responsible AI principles.
Single-View Compliance Posture
Organizations need a unified enterprise view of their compliance posture, featuring automated approval workflows and action points for each use case and regulation. This strategy helps efficiently identify compliance gaps across different AI use cases globally.
Centralized Repository of Compliance Regulations
To streamline compliance across varied use cases and regions, enterprises should establish a comprehensive data library containing compliance policies, processes, and requirements. This library can address diverse AI use cases within an integrated solution.
Agile Compliance Solution with Automatic Updates
The solution should allow users to make continual updates to policies easily, adapting to new and revised regulations.
Easy-to-Use Embedded Regulatory Content
Regulatory content must be easily integrable into existing workflows, with guided compliance tasks to facilitate efficient evidence capture by use case owners.
Multi-User Compliance Assessment Solution
The solution should enable seamless collaboration among stakeholders through a governed multi-user compliance assessment cycle, minimizing the need for manual processing.
Accelerating Regulatory Compliance Processes
To address these challenges, organizations can utilize compliance accelerators—a comprehensive, prebuilt list of AI regulations, frameworks, and obligations. This resource enables AI use case owners and compliance teams to quickly identify compliance obligations and mitigate potential noncompliance risks.
Compliance accelerators function as a data-as-a-service library, helping organizations streamline their compliance processes, enhance accuracy, and foster confidence in addressing applicable regulations.
By implementing these solutions, compliance teams can work more efficiently, focusing on critical tasks while automating time-consuming activities, such as documenting and researching AI compliance requirements for each use case. This tailored approach helps meet the specific business needs of each AI use case and the regulatory demands of various operational locales, all within a cohesive AI governance framework.
