While migrating infrastructure and applications to the cloud presents businesses with agility and scalability, many are discovering it hasn’t delivered on cost savings, finding themselves saddled with tech debt in the form of SaaS platforms and apps, accumulated over time.

Accessing data from these systems is proving to be time-consuming and expensive, delaying time to insights and stifling innovation. It’s also hindering investment in data-hungry AI solutions.

The situation is compounded by the need for more airtight data security solutions capable of analysing massive amounts of complex security data to detect threats and anomalies, and mitigate the impact of data breaches.

For some time now, organisations have been adopting security data lakehouse solutions that could be integrated with their data management and analytics platforms. The most effective of these solutions leverage autonomous indexing and caching technologies, designed to quickly find relevant data across any dimension, source or index.

However, the explosion of tools for managing security data has led organisations to explore a more federated approach to consolidate their data management and security posture across on-premises and hybrid architectures. This has allowed them to deploy security data platforms that augment their existing security stack.

The benefits of on-premises AI

Before we get into the specifics of data security, let’s return to the topic of on-premises data and why more organisations are reverting to on-premises or hybrid cloud data strategies. Many highly regulated industries, including financial services, healthcare, and the public sector, operate within on-premises or hybrid environments. This isn’t always down to compliance either; it’s by choice, because it allows them to manage their data in a governed, compliant, and controlled manner. 

On-premises also provides the data foundation for AI models and apps, and crucially, the infrastructure needed to run AI workloads. Organisations can build their own AI data stack on-premises using an AI data architecture that will provide the data sets needed to train their own models and agents. It will allow businesses to integrate agents into their daily data workflows, enabling natural language queries across data sets to accelerate insights and perform tasks across multiple domains.

In addition, keeping data on-premises reduces the risk of security breaches. It provides organisations with greater control over their infrastructure and, in the case of an on-premises AI solution, complete control over how and where AI data is processed. It also contributes to greater AI data governance, ensuring compliance with evolving AI standards and regulations. Also, cloud migrations can be costly and time-consuming, whereas AI initiatives developed on-premises are faster to implement.  

Federated approach enhances data security

As I alluded to earlier, increased AI integration, especially in highly regulated industries, has prompted the need for more comprehensive data security solutions. While keeping sensitive data on-premises contributes to a more secure environment, organisations are constantly at risk from threats across different attack vectors. As a result, many enterprises are now adopting a security data platform approach that can be used to enhance their existing security stack for SOC, Cloud SIEM and other cybersecurity use cases.

This approach provides enterprises with centralised, secure access to data that includes end-to-end governance and compliance. It provides data and security teams with a secure infrastructure to run federated queries across multiple data sources without compromising data privacy, governance or compliance.

The platform approach allows organisations to log queries and securely pull in telemetry data in the form of logs, metrics and traces from across their cloud and on-premises IT stack. They can perform real-time monitoring and logging to collect and analyse massive amounts of complex security data, which can be translated into actionable recommendations, in real-time.

Crucially, it can be deployed across on-premises and hybrid cloud architectures, adding another layer of protection to secure on-premises systems that are hosting the data foundations for data analytics and AI.

Simplifying data access across complex environments

Managing data on-premises, building an AI data stack to power new AI innovations and improving data security can be achieved by embracing the concept of data products - a set of tools that empower business functions to solve problems quickly and with agility.

Traditionally, enterprises have been hampered by legacy solutions and fragmented data that can be slow and expensive to access. Data products span multiple data resources, enabling businesses to transform, curate and share business-critical data sets within minutes. The use of data products also makes data more accessible to non-technical users, reducing complexity and speeding the time to insight. 

This new type of architecture offers substantial cost savings and improved operational efficiencies by enabling access to data directly, regardless of where it resides. This model is applicable to both on-premises and hybrid environments and comes at a time when enterprises are looking to reduce overheads and lower infrastructure costs.

The holistic approach to data access and management allows organisations to optimise their operations and maximise their resources effectively. It ensures greater accessibility, increased reliability, and security, leading to faster insights and better use of data.

Justin Borgman CEO Starburst