The need to design a secure and compliant data & AI strategy

Business leaders looking to adopt data-first strategies face significant challenges, such as the challenges associated with new technologies like AI models

Today, organisations often apply data-driven approaches sporadically, leaving value on the table and creating inefficiencies. McKinsey & Company predicts that, by 2025, nearly all employees will naturally and regularly leverage data to support their work. 

This transition to a data-driven mindset will enable organisations to make better decisions as well as automating basic day-to-day activities and regularly occurring decisions. Employees will be free to focus on more ‘human’ domains, such as innovation, collaboration, and communication. 

However, business leaders who are looking to adopt a data-first strategy are facing significant challenges: from the increasing pressure and demands from customers to invest in new technology, such as AI, to the requirement of satisfying stricter and increasingly complex data security, data governance and regulatory requirements.

To navigate these challenges, there are a number of critical steps a data-driven business must take. At the centre and as a necessary first step, they need to choose a modern, data and AI cloud platform with governance, security and compliance baked into its very foundation. By leveraging such a data platform, businesses can focus on their core competencies, accelerate insights from their data and create new end-user experiences. 

Tackling compliance challenges 

Increasing regulatory requirements are becoming front and centre for any modern, data-first business strategy, explains Artin Avanes, Director of Product Management at data cloud company Snowflake. “This is true for all businesses including those looking to scale within a single geography or regulated industry, and even more so for global businesses looking to operate and scale across different geographies and diverse industries.” 

Today, regions such as the US, Europe and Asia have introduced numerous data management and data governance regulations that organisations must adhere to. 

“For example,” Avanes says, “in the US, there is no singular law that covers the privacy of all types of data. Instead, it has a mix of laws that are designed to target specific states or specific types of data. For example, the California Consumer Privacy Act (CCPA) gives California residents increased transparency and control over how businesses collect and use their data, while the Gramm-Leach Baily Act (GLBA) covers consumer financial products and requires companies to explain how they share data. In the EU, businesses must adhere to the GDPR, and in Asia, organisations have to comply with the Information Technology Act.”

The need for an integrated, consistent data governance strategy 

In order to navigate continuously evolving regulatory requirements, Avanes suggests organisations must implement a data governance framework that empowers them to discover, understand and protect their heterogeneous data while leveraging it securely to collaborate internally and externally. 

“An effective, all-encompassing data governance strategy will enable organisations to store and manage personally identifiable information (PII) and other sensitive data securely while monitoring and protecting that data in near real-time,” he describes. “This includes modifications as well as new incoming sensitive data without the need to manually intervene and adjust existing secure workflows. 

“The emergence of modern data applications paired with the need to enable global collaboration poses an additional complex challenge to governance and security. A modern data platform allows its users to seamlessly integrate, apply and enforce the aforementioned core security and governance platform capabilities. It accelerates modern and global data applications development and enables application builders to focus on their core competencies and monetise opportunities with peace of mind.”

The future-proofing of data strategies

A scalable and efficient data governance strategy must also be forward-looking. Technology is advancing rapidly, making it more challenging for organisations to keep pace with security and governance advancements. As a result, businesses must think about the foundations and frameworks that will apply to technology in the years to come. 

“Take generative AI and the emerging large language models (LLMs) as recent popular examples,” Avanes says. “Over the past few years, AI has swiftly become a crucial aspect of modern life, transforming the way we live, work and interact with each other, with many believing it will be one of the most profound technology shifts seen in our lifetimes. Organisations need to stay nimble, with a security and governance framework that can easily adapt to such innovations.”

As more businesses leverage LLMs, the models will adopt more sensitive and private data to learn from. “However, the models are at risk of breaching or violating diverse compliance requirements. Take GDPR as an example. Once a model is trained, it will continue to use that data, with no process in place currently to have this data removed. 

“While the forthcoming European Union AI act will likely take this into account, the laws and regulations around data use and governance will keep changing, and companies need to be in a position to respond with ease and at scale via proper automated security and compliance workflows. Increased automation around security and compliance reduces the likelihood of causing disruption to existing processes, products, and experiences, while the absence of manual and error-prone intervention decreases the risk of security and compliance violations.”

Scaling compliance across data teams 

To scale a data strategy, businesses must have the right teams in place who work together effectively,” Avanes asserts. “Typically, there are domain experts in security, governance and compliance working with IT, and data teams who are responsible for modernising the tech stack. Often, these groups are separate, with a different level of understanding around security and compliance, which can often result in friction with data modernisation initiatives.” 

For example, data platform teams may be keen to adopt the latest technologies to keep pace with the competition or build new products and experiences, but the compliance team might be reluctant due to potential violations of existing regulatory requirements. 

“We are seeing a trend where forward-looking, data-driven organisations ensure that both these teams work very closely together so that they can share an understanding of these challenges and arrive at the right solution in order to scale the strategy with minimal disruption. Data stewards and compliance officers are deeply embedded within data platform teams and closely collaborate with data architects.”  

A modern data platform must offer a first-class search and discovery experience, including the ability to manage uncurated data, independent of its data format such as structured or unstructured. “It needs to scale with data volumes and data changes by ensuring automated classification of all data assets,” Avanes concludes. “This should feed into scalable and consistent data access policies the data steward can define and enforce. These are all core security and governance platform capabilities.”

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