We all know that data is an incredibly valuable asset to any organisation, especially when it comes to cybersecurity. It can also be the root of some significant overheads.

Generally speaking, the more data your business collects on the events occurring within its digital environment, like logs, traffic, and user behaviour, the better its overall security posture will be. But collating, processing, and storing that data costs money.

As cyber threats get more sophisticated and more ubiquitous, balancing comprehensive visibility with ever-tightening budgets is a serious challenge—one with potentially dire consequences for those who fail to strike that balance well.

So how can businesses manage and maintain critical cybersecurity data without breaking the bank?

The cybersecurity spending crunch

Cyberattacks are mounting in both scale and complexity, powered at least in part by the increasing use of AI by bad actors. Most organisations are painfully aware of this growing need to bolster their security defences, and the good news is that data suggests that spending on cybersecurity is on the up.

According to our research, 90% of organisations plan to increase their investment in cybersecurity this year, with just 10% of security leaders reporting that spending will remain at previous levels or even decrease.

With investment in cybersecurity going up almost across the board, these upturns in spending may still not be enough to protect businesses against the rising tide. In fact, a massive 59% of security leaders still feel their organisation isn’t budgeting enough for cybersecurity.

The reality is that more spending on advanced, next-generation technologies is needed to combat the new wave of AI-fuelled attacks, but this undeniable requirement for more resources is bumping up against the need for organisations to manage their bottom lines in tricky economic conditions.

As a result, cybersecurity leaders are having to find ways to maximise their budgets and do more with what expenditure they’re allowed. And although cybersecurity solutions are becoming more accessible, the costs involved can still be restrictive for many organisations—costs like managing and storing huge amounts of security-related data.

Balancing visibility and budgets

The volume of data that businesses need to process to maintain their security is skyrocketing. Organisations are more connected than ever before, with more endpoints, devices, and third-party connections to manage than ever before. By 2025, enterprises will be managing 250% more data than in 2020.

With such vast quantities of data now available for analysis, we asked CISOs whether they felt they had sufficient oversight of their environments. And yet, almost a third (63%) of respondents believe that their organisation does not have enough visibility over devices, networks and applications. Improving visibility over these IT systems and networks was cited as a top priority for 39% of security leaders in the coming year.

Improving visibility is a key factor in creating stronger defences. But increasing visibility means ingesting more data. And more data means more spending…

Making data management more cost-effective

When data keeps multiplying but budgets remain finite, security leaders need to devise smart management strategies to make sure they’re getting maximum value from their data at minimum cost. Let’s take a look at some practical ways businesses can reduce their data management spending.

Use tiered storage solutions

Data storage doesn’t have to be one-size-fits-all. Using a single type of storage is rarely the best option for managing data, and certainly not the most cost-effective one.

Data that’s only being kept for regulatory compliance reasons (the digital equivalent of those dusty boxes of records on the top shelf that no one ever goes in), for example, doesn’t need to be accessed as often as other, more relevant types of data.

That means it can be stored using hosting options that restrict the amount of access you have to your data in exchange for better rates—a bit like a savings account.

By categorising data based on key factors like required access frequency and importance, it can be stored accordingly in different tiers of storage solutions, shaving money off your storage spend in the process.

This categorisation process may take a little time to set up, but it’ll save you significant amounts of cash in the long run and can be applied to all data sets going forward.

There are lots of cloud-based services that offer tiered storage plans. Amazon S3, for instance, lets you choose from Standard, Infrequent Access, and Glacier tiers, all with varying costs associated. Be sure to regularly review and update data storage policies to ensure data is stored in the appropriate tier (more on that shortly).

This approach cuts costs by only using high-cost storage when necessary, while leveraging cheaper options for data that doesn’t need to be as immediately available.

Roll out a Data Lifecycle Management (DLM) plan

Data Lifecycle Management involves managing data throughout its existence within your environment, from creation to deletion, according to predefined policies. Putting these policies in place helps reduce data overheads by making sure that data is hosted in the most cost-effective storage tiers, and that any data that’s no longer needed is scrubbed.

Some initial DLM tasks to focus on include:

• Setting data retention periods for specific types and categories of data
• Automating the archiving of data that meets certain criteria
• Ensuring that obsolete or redundant data is systematically removed

These DLM policies should be communicated and enforced across the organisation; without consistency, you won’t reach your strategy’s full cost-saving potential.

There are DLM tools and software out there to help you automate the process of archiving, migrating, and deleting data based on its age, relevance, regulatory requirements, or any other custom characteristics that you’d like to apply. Some of the most popular choices are Commvault, Veritas NetBackup, and IBM Spectrum Protect.

Implementing effective DLM policies massively reduces the amount of data that needs to be stored and managed, thereby lowering storage costs. Plus, better data lifecycle management also helps ensure that you’re staying compliant with any relevant privacy laws and data regulations, reducing the risk of being hit with potential fines.

Optimise your data processing workflows

Streamlining and optimising your data processing workflows can also help cut costs. This might involve removing redundant processing steps, using more efficient algorithms, and leveraging cost-effective data processing tools and platforms that reduce the amount of time you spend actively handling and processing data. This will not only cut down on the use of as-a-service resources, but also free up your cybersecurity team to work on more valuable tasks.

The first step towards more streamlined data operations is conducting an audit of your current data processing workflows to root out any potential inefficiencies. You can then address any areas for improvement by adopting data processing frameworks and tools that are known for their cost-effectiveness (like Apache Spark for distributed data processing) and taking advantage of serverless computing options for on-demand scalability.

All of this tweaking and optimising will culminate in a reduced computational load, meaning lower spending on data processing infrastructure, human resources, and energy consumption.

Take control of data management costs with Cribl

If you’re looking to take control of your data management costs, the above strategies are no-brainers that will save you money, and make your data operations more efficient, for years to come.

But if you want to see serious reductions in your data-related spending without sacrificing performance, you should consider implementing a data management solution like Cribl.

A leading data observability platform, Cribl equips businesses to manage and process mammoth volumes of machine data more efficiently. By filtering, routing, and optimising data streams, Cribl reduces storage and processing costs, boosts data quality, and enhances the performance of analytical and security tools—all of which equates to lower data management expenses.

Here are just a few of Cribl’s most impactful data management features, and how they can help your organisation reduce its data management costs.

Data reduction and filtering

Cribl allows businesses to filter and reduce the volume of data before it reaches expensive storage and analytics platforms. Since users can choose to route only the most relevant and necessary data, you can avoid storing and processing redundant or low-value data, and racking up associated costs.

Log and metric routing

With its advanced log and metric routing capabilities, Cribl enables businesses to differentiate different types of data before sending it to the most appropriate and cost-effective destinations. High-value data, for example, can be set to be sent to premium analytics platforms, while less critical data can be routed to cheaper long-term storage options. Thanks to this strategic routing option, you won’t find yourself overpaying for storing or analysing data that doesn’t need to pass through high-cost resources.

Data enrichment and transformation

Cribl offers tools to enrich and transform data in transit, meaning data can be formatted, cleansed, and augmented with additional context before it reaches its final destination. By performing these preprocessing operations on the fly, businesses can reduce the workload on downstream systems and reduce the need for extensive post-processing in more expensive data environments.

Centralised data management

Bringing together data streams from various sources, Cribl gives businesses a single interface through which to manage data. This centralisation streamlines the data management process, reducing the complexity (and costs) that inevitably come with maintaining multiple disparate systems. And simplified management means less administrative spending and fewer resources spent on maintaining and integrating siloed data platforms.

Optimised observability pipelines

One of Cribl’s specialities is optimising observability pipelines, including those that handle data collected from logs, metrics, and traces used for monitoring IT systems. More efficient management of these pipelines ensures that only valuable and actionable data reaches your expensive observability and monitoring tools, and reduces the amount of data processed and stored in them.

Start slashing your data costs with Cribl services from RiverSafe

With RiverSafe in your corner, you can tap into the tools and expertise you need to get your data management costs under control. Our professional services are designed to help you make the most of your data pipelines and turn data streams into valuable, actionable insights.

Equipped with a deep understanding of Cribl platforms, we specialise in boosting the power of Cribl products to elevate your data capabilities and help you take the next steps on your journey to data excellence.

Start your Cribl journey today