When it comes to keeping businesses safe from cybersecurity risks, the challenges are increasing all the time. Hackers are constantly coming up with new ways of attacking enterprises, and today's threats are larger and more complex than ever.
However, the good news is that defenses are also becoming more advanced in response to this ever-evolving situation. For example, technologies such as automation, artificial intelligence (AI), and machine learning are better able to respond to cyberthreats quickly and take some of the burden off IT personnel.
But to do this, you must have full visibility into a company's ecosystem. This means being able to gather data from across your network, process it, and derive useful insight that can be used to enhance the company’s security.
The growing threats faced by businesses
The threats posed to businesses by cybercriminals are higher than ever. For instance, the first quarter of 2020 alone saw 8.4 billion records exposed around the world - a 273% increase from the first six months of 2019.
What's more, 80% of data breaches included personally identifiable information, at a cost of $150 per record. This means that for large enterprises with more than 25,000 employees the average total cost of a data breach in 2020 stood at $5.52 million.
One reason for this is today's networks offer hackers more potential entry points than ever. In addition to greater use of mobile devices such as smartphones and tablets, the number of Internet of Things (IoT) devices connected to the network is growing exponentially. These new devices - which often lack the same robust security defenses as traditional equipment - provide hackers with many ways to access a network.
Therefore, it's no wonder that research shows around a quarter of security incidents (26%) can be traced back to unsecured connected devices.
Why you need a data-driven security strategy
The first step in protecting this vast array of endpoints is to have full visibility into everything that's happening across them. And this means you need big data solutions in order to cope with the vast quantities of information and traffic these devices generate.
For instance, monitoring activity through your IoT and other endpoints will allow you to identify anything unusual. Being able to spot suspicious actions across your entire network could be the difference between stopping an attack early and a major data breach that costs you millions.
But as is always the case when dealing with such large quantities of data, this is easier said than done.
What you need for big data to be effective
An effective big data strategy is about much more than the data itself. You also need the right environment to collect, store, and process this effectively and ensure you're able to generate real-time insight. Therefore, any advanced threat intelligence solution needs a few key elements when it comes to managing data in order to be successful. Here are three steps to make the most of big data.
1. Easy access to data
Big data won't be useful if personnel or applications can't access it quickly or easily. If the relevant data for your threat warning system is distributed over a network between hard-to-integrate silos, you won't get the full picture.
Instead, you need a centralized platform that allows unfiltered access to all your endpoint data. This isn’t only essential in looking for threats proactively, but also being able to coordinate an effective company-wide response to any incident.
2. Continuous recording
In order to identify and react to threats in real-time, you need a continuous picture of every aspect of your network. If you're missing certain periods or endpoints, you won't be able to prove that any malware has slipped past your perimeter defenses and into your wider network.
This is also essential in aiding tools such as AI threat detection. If an issue is detected, these tools can automatically analyze the rest of the network to determine if they’ve been breached in any other location, but if the AI doesn’t have a complete picture of activity, it will be unable to do this with any accuracy.
3. Long-term data retention
Another key benefit of big data is how it can be incorporated into machine learning tools. When it comes to security, this can be used to build up a more accurate picture of what normal activity looks like, allowing you to quickly spot any anomalies and reduce the risk of disruptive false positives.
To do this, tools will need a comprehensive archive of activity to build from - which in turn may require a thorough data retention policy to ensure all regulations are being followed for the collection, storage and access of this information.