In just a short time, data has become the backbone of day-to-day operations for many organizations. This shift is largely thanks to the rapid growth of the Modern Data Stack, led by key players like Snowflake, Databricks, and BigQuery. Over the past decade, more and more businesses have moved their analytics and AI workloads to the cloud, benefiting from its scalability and cost efficiency. This move has transformed how companies operate, enabling them to seamlessly integrate AI and analytics into their products, services, and processes at an unprecedented pace.
However, as the Modern Data Stack became more widely adopted and integral to daily operations, its complexity also increased, bringing with it a host of new data security risks.
The Modern Data Stack and its components have been plagued with security risks and incidents. Just to name a couple:
However, LLM technology also comes with significant security risks, especially when using Retrieval Augmented Generation (RAG) to grant your LLM access to company data because:
In short, Cloud has significantly democratized access to insights through AI and Analytics, but this has come with significant security vulnerabilities. The resulting data breaches expose organisations to costly litigation and regulatory fines, which not only strain budgets but also divert valuable time and resources away from strategic initiatives.
In a recent daunting case, Hot Topic suffered a massive breach where the data of 54 million customers was compromised. The attack occurred through the computer of an employee at Robling, a data integration service. The computer which was used to access Hot Topic’s Azure, Snowflake and Looker environments, was infected with malware which allowed the hacker to extract the employee’s credentials to Snowflake and Looker. As MFA wasn’t turned on, the hacker was able to extract 730GB of personal data from Snowflake.
As data is increasingly playing an important role in daily operations and into making AI Agents performant, it has become imperative to safeguard its security through:
Our customers use Raito to help streamline data security workflows for AI and Analytics. With Raito, data consumers get access to insights in a fact and secure way. Our unique architecture guarantees a fast time to value while also supporting mature use cases with dynamic and automated data security management.
From the first moment, you connect Raito to your multicloud data environment, you can centrally monitor data access and usage, monitor your data security maturity, and detect and remediate data security risks. By correlating identity information across accounts, Raito gives you an identity-centric view on user access and usage.
Dynamic data teams federate data security responsibilities to data product owners to achieve a productive balance between data security and data access. Federation prevents the central data team from being flooded with data access requests, allowing them to focus on their work without undue hold-ups. Raito’s user-friendly interface lets data product owners manage access without requiring technical expertise.
Automation plays a crucial part in scaling data security. It helps you save time and reduces the risk of errors that come with manual processes. Automatically detecting and prioritizing risks, auto-approving and auto-revoking access, and dynamically masking and filtering data using tag-based policies are essential to striking a healthy balance between data security and data access.
