Maintaining consistent data security is difficult because so many products provide siloed security controls, use proprietary data classification, act on specific repositories or processing steps, and do not integrate with each other. This restricts organizations’ ability to identify and deploy adequate, and consistent, data security controls while balancing the business need to access data throughout its life cycle.
2024 State of Data Security Report
Our 4th annual report surveyed 700+ data leaders for their perspectives on AI, data security, visibility, and governance.
It can be difficult to ensure that sensitive information is protected from potential data leaks. Additionally, the emergence of generative AI and GPT technologies has brought about new concerns regarding data privacy and security.
We are seeing a rise in compliance and privacy regulations. This trend potentially can slow the ability for an organization to use data, as well as increase the need for data access and governance solutions to prevent any potential fines or enforcement actions.
If you don’t automate your security and simplify it, the biggest problem you’re going to have is some human making a mistake.
Swedbank needed to build an enterprise-scale advanced analytics platform that would also enforce trust in our security, management, and access to data internally while protecting our customers’ assets and data.
In the age of cloud and AI, data security and governance complexities are mounting. It’s simply not possible to use legacy approaches to manage data security across hundreds of products.
The future of our business depends on making our data and AI initiatives successful.
Data Security Is a Bigger Priority Than AI for 2024
Despite the buzz about AI, 35% of data professionals say the major initiative their organization will take on in 2024 is implementing stronger governance and security controls, compared to 20% that said integrating AI into business processes. At the same time, data security budgets and capabilities are improving.
When enterprises decide to shift data to the cloud or implement a data mesh approach, they implement security strategies to protect their organization through these changes. But by the time they’re ready to go to production, they already need new security strategies in place—that’s how quickly strategies need to evolve.
AI Brings Great Excitement – And Risk
88% of data professionals say that their employees are using AI, but 50% agree that their organization’s data security strategy is failing to keep up with its rapid evolution. While many are confident that AI will help with security-related tasks like anomaly detection, 56% say their top AI concern is exposure of sensitive data via a prompt.
Over half of respondents (56%) cite the exposure of sensitive data via an AI prompt as their greatest area of concern around AI. Despite this, few organizations are taking steps to manage this risk through policies and governance.
Data Access and Visibility Are Top Security Challenges
33% say a lack of visibility into data sharing and usage is their biggest security challenge, and 56% report missing business opportunities due to access control issues. Still, 94% indicate some level of confidence in detecting and responding to threats – a disconnect that could signal overconfidence.
If visibility is a major issue for at least a third of data leaders, how can 94% of them proactively spot and address threats? Data professionals need streamlined security solutions in place to maintain confidence and keep data visible and protected.
Managing Data Security Is a Team Effort
As data architectures become more complex, more people and technologies are involved in managing security. A third of data leaders report that 20+ people are involved in data security, and despite bigger budgets and better strategies, 57% say their tools and tech definitely need improvement.
Rather than waiting to discover security issues in the production environment, organizations are pushing security testing earlier and implementing data security measures throughout development.
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