It is no surprise that integrating generative AI is the new mandate among business leaders. The buzz is not unfounded, as Gartner reports that 45% of executives say that ChatGPT has prompted an increase in AI investment, and 70% of organizations are currently exploring generative AI. However, what is not talked about enough is just how many companies are being left in the dust due to a lack of proper data controls and procedures when leveraging generative AI.
This groundbreaking technology is creating far more challenges for data leaders than expected, especially when it comes to finding the right equilibrium between fostering data-driven innovations and fulfilling data obligations.
Navigating AI governance: Addressing compliance, security and ethics
Although AI is offering exciting opportunities to introduce new capabilities, streamline processes and minimize manual tasks, it continues to be a topic for concern around governance. Organizations are dealing with changing data privacy regulations, increased security hurdles and the ethics conversations associated with handling enormous amounts of sensitive information. Naturally, a major challenge arises trying to ensure regulatory compliance and protecting against data misuse or unauthorized access.
In this context, compliance involves investigations into AI use, which is focused on privacy, content moderation and copyright concerns. The consequences are severe and far-reaching—organizations can face harsh legal penalties by regulatory authorities, which can ruin them financially. The misuse of data can harm a company’s reputation and undermine stakeholder trust, resulting in long-term losses in consumer loyalty and business opportunities. Beyond that, due to organizations’ concerns over data breaches and exploitation, non-compliance can even risk the elimination of AI implementations altogether, hindering their own modernization and innovation roadmaps.
Collaborative steps in governance to foster AI’s potential
Effective governance of this emerging technology hinges on collaborative efforts across privacy, governance and security domains. As these elements merge, they provide the bedrock for maximizing AI’s potential. In the current AI-focused landscape, establishing strong data oversight policies and controls is a must. By integrating insights from experts in these domains, organizations can orchestrate streamlined and ethical AI deployment.
In order to effectively work across these silos, and to mitigate risks when harnessing generative AI, there are several key steps that organizations must take, including:
- Developing integrated policies and procedures: Besides ensuring strict compliance, collaborative development of tailored plans for each organizational pillar of security, privacy, compliance and governance establishes a robust cross-silo foundation. This approach optimizes operations, minimizes conflict and underscores the organization’s adaptability to changing regulations.
- Regularly assessing risks and vulnerabilities: Conducting joint risk assessments exemplifies proactive risk management. It involves defining goals, gathering data, identifying critical assets and analyzing potential threats. This guides precise mitigation strategies, including security measures and contingency plans, with continuous updates for relevance. Collaboration across departments is key, fostering expertise-sharing and crisis readiness. Ultimately, these assessments showcase dedication to proactive risk handling, enhancing resilience and team agility for stable operations.
- Leveraging technology: Strategic technology integration is essential for the advancement of modern organizations. Data protection and general organizational efficiency can be improved by investing in technology solutions that seamlessly integrate privacy, governance and security efforts; help streamline procedures; and optimize resources, giving organizations the tools they need to deal with compliance and risks in their digital environments. To achieve seamless integration, compatibility with current systems and scalability should be prioritized. Pilot projects and thorough testing can assist in ensuring that new technology is compatible with an organization’s existing workflows and overcomes any potential issues. Following deployment, regular monitoring and assessment are critical in ensuring that the technology continues to meet its intended objectives and advances the organization as a whole. Automation and high-efficacy data intelligence can serve as force multipliers in these efforts and improve the accuracy of risk assessments.
Through collaborative efforts aimed at crafting comprehensive policies and procedures, encompassing the requisites of generative AI—privacy laws, governance standards and security best practices—companies can confidently align their operations with these vital benchmarks.
Undertaking collective risk assessments tailored to the nuances of generative AI serves as a proactive measure, empowering teams to identify and tackle potential vulnerabilities before they escalate. Moreover, strategic investments in bespoke solutions tailored to generative AI, unifying privacy, governance and security efforts within an automated platform, not only enhance protection but also streamline compliance and bolster overall operational efficiency.---
About the author:
Michael Rinehart is the VP of Artificial Intelligence at Securiti.ai. Prior, Michael was a chief scientist at Elastica/Blue Coat Systems leading the design and development of many of its data science technologies.
He has deployed machine learning and data science systems to numerous domains, including internet security, healthcare, power electronics, automotive and marketing. Prior to joining Elastica, he led the research and development of a machine learning-based wireless communications jamming technology at BAE Systems.
Michael has a BS from University of Maryland and an MS and PhD in Electrical Engineering from the Massachusetts Institute of Technology.