In today's data-driven world, organizations of all sizes and industries rely on vast amounts of data to drive business growth and make informed decisions. However, with the rise of artificial intelligence (AI) and generative AI technologies, ensuring the security and protection of sensitive data has become increasingly crucial.
According to a recent survey conducted by Privacera, a leading AI and data security governance company, 98% of businesses prioritize a consistent approach to security and governance. However, a majority of these organizations (64%) use different security tools for various use cases. This fragmented approach can lead to inconsistencies, lack of observability, and potential security gaps.
Generative AI technologies are rapidly advancing, but they also pose risks such as sensitive data exposure, intellectual property leakage, model abuse, and regulatory compliance failures. In the same survey, 49% of respondents expressed concerns about potential vulnerabilities from using AI, while 39% were worried about abuse and data bias. Erosion of customer trust due to generative AI implementation was a concern for 37%.
Moreover, companies often adopt multicloud solutions and utilize multiple AI models simultaneously, making it essential to have a unified data security governance strategy that protects and secures data and AI models, regardless of where they are stored, deployed, or operationalized.
Data security governance should encompass well-defined structured datasets, semi-structured and fully unstructured data assets, as well as the protection of AI models. Comprehensive controls need to be in place to safeguard data throughout its lifecycle, from ingestion to consumption.
Prioritizing AI safety and security is not only vital for organizations aiming to derive value in the long term but also to comply with existing and upcoming regulatory mandates. The White House executive order released in October provides further impetus for organizations to take action and ensure the protection of sensitive data.
Building a unified data security governance strategy requires collaboration among all stakeholders, including the CEO, CIO, CDO, and CISO. Connecting business strategy to data strategy starts with aligning goals and key performance indicators. The focus should be on achieving business outcomes rather than simply adopting new technologies.
To enhance data security posture, organizations must ensure that their policies and regulations cover both regular analytical use cases and generative AI applications. Personal identifiable information data should be protected at every point of user interaction, regardless of the AI technology being employed.
Achieving a comprehensive data security and governance strategy involves providing visibility to potential risks across AI and other forms of data. This involves educating and empowering teams to make responsible data decisions. Automation and systematic guardrails can help establish consistent security strategy implementation.
As organizations navigate the evolving landscape of AI and data security, a unified approach is crucial. By prioritizing data security governance, organizations can protect sensitive data, mitigate risks, comply with regulations, and ultimately drive successful business outcomes.
While generative AI technologies bring new challenges, they also present an opportunity for organizations to re-evaluate their data security strategies and prioritize comprehensive governance. By embracing this opportunity and adopting a unified data security governance strategy, organizations can confidently harness the power of AI while safeguarding their most valuable asset: data.
