New Regulatory Compliance Laws and Regulations are Emerging Quickly
New risk management needs are constantly arising as regulatory compliance laws and regulations continue to evolve. To be successful business need to integrate responsible risk management practices into everyday operations, especially for emerging disruptive technologies such as AI. As these laws and regulations evolve businesses must be aware of them and adapt to them to be successful.
Data Governance is driven by business needs including those of compliance laws and regulations. The rapid rollout of new compliance laws and regulations is being driven largely by privacy needs, by the huge growth in data being generated and used and by emerging technologies. Examples of new laws and regulations impacting how companies are required to manage data and privacy are:
- GDPR – (General Data Protection Regulations)
- DMA – (European Union’s Digital Markets Act)
- DPF – (EU-U.S. Data Privacy Framework, which replaces the EU-U.S. Privacy Shield)
- Executive Order – E.O. 14086 – (Enhancing Safeguards for United States Signals Intelligence Activities)
- CPRA/CCPA – (California Privacy Rights Act which amends the California Consumer Privacy Act)
- EU AI Act 2024 – (European Artificial Intelligence Act 2024)
The Intersection of Privacy and AI Governance
The following Venn diagram is a quick way to visualize how compliance laws and regulations intersect with AI Governance overall.
(For further elaboration on specific terms found within the above Venn diagram please refer to the IAPP’s Key Terms for AI Governance and Glossary of Privacy Terms).
Data Governance Implementation Challenges
Gartner (a highly regarded technical market research and consulting company) has done market research surveys which highlight several challenges in implementing effective AI governance:
- Many organizations still lack a dedicated governance framework.
- Surveys indicate that over half have not yet implemented one, though many are in the process of developing one.
- Fragmentation within departments can lead to inefficiencies and inconsistencies in governance practices, making it essential for organizations to adopt a unified approach.
Recommendations for Effective Governance
To navigate these challenges, Gartner suggests:
- Establishing a cross-functional governance team that includes technical experts, ethicists, compliance officers, and business leaders to oversee AI initiatives.
- Focusing on user trust and transparency by documenting decisions related to AI usage and data handling.
- Collaborating closely with legal and compliance teams to align governance strategies with regulatory requirements.
As organizations increasingly rely on AI technologies, implementing a comprehensive governance framework is essential for ensuring responsible use and compliance with evolving regulations. To implement and manage this framework businesses are changing their organizational structures and C-suite roles and members.
Emerging Roles, the Evolution of C-suites
Businesses and their C-suites are adapting and adjusting to new challenges brought by new compliance regulations and the emergence of AI. As the growth in data and data sources has occurred businesses have had to evolve and grow their organizational structures to successfully manage and keep pace with the increased security and compliance needs. They have done so by adding additional executive roles that specialize in managing the increasing business challenges while properly managing this growth in data and their associated data analysis, security, and compliance needs.
Earlier on businesses had the roles of:
- CIO – Chief Information Officer
(Responsible for all software, hardware and infrastructure, as well as with the overall management of an organization’s data) - CTO – Chief Technology Officer:
(Tends to focus on developing technological solutions to meet customer needs).
Today businesses have of necessity evolved and added more roles to help with managing and leveraging data and to deal with the increasing needs for data analysis, data security and data compliance. These new roles include:
- CISO – Chief Information Security Officer
(Many organizations have a chief information security officer, or CISO, as well as a CIO. As the name implies, a CISO has a more limited role, covering security issues). - CDO – Chief Data Officer
(Most frequently seen). - CAO – Chief Analytics Officer
(Less common. CAOs report to other first-level CXOs, or directly up to the CEO. They focus on solving problems for decision-makers by using data, technology, and analytics techniques). - CDAO – Chief Data and Analytics Officer
(More frequently seen recently. Refers to the business leadership role that has the primary enterprise accountability for value creation by means of the organization’s data and analytics assets, as well as the data and analytics ecosystem. Combining CDO and CAO, the CDAO role combines two jobs, CDO and CAO, into one. Often there is a 50-70% chance for a CDAO reporting to a CIO, because of the gravitational pull from an IT organization, i.e. the number of employees, size of budget, status quo organizational setup. Most of the CDAO’s time is spent on data management, focus of a CDO, rather than analytics value creation and business impact delivery, the focus of a CAO). - CAIO – Chief AI Officer, or Chief Artificial Intelligence Officer
(Emerging. CAIO can also be labeled as CDAIO, Chief Data and AI Officer. AI is the more polished word to replace (and be an extension of) “analytics.” It is more sophisticated, technology-enabled, scalable, deeply rooted in operational process and human behavior, more forward-looking, and potentially leading to fundamental changes in organizations).
References:
- The Intersection of Privacy and AI Governance
Link is here. (IAPP is the International Association of Privacy Professionals). - The what, why and how of AI governance in 2024
Link is here.
- Gartner AI Governance Trends 2024
Link is here.
- EU AI Act 2024
Link is here.
- What are the key differences between CDO, CAO, CDAO and CAIO?
Link is here.
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