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Cybersecurity has been a concern since shortly after the invention of the internet, but it’s arguable that it’s never been more serious than it is today.
Every cybersecurity body agrees that 2021 saw hacking attempts jump: according to one report, Q4 2021 saw over 900 attacks per organization per week, an all-time high, and that weekly attacks in 2021 overall rose 50% over 2020
The average cost of a data breach in 2021 reached $4.24 million per incident, higher than it’s been in 17 years, and ransomware costs are predicted to hit $265 Billion by 2031, with a new attack coming every 2 seconds.
All of which makes it clear that organizations can’t afford to let their guard down for an instant. You need to bring every weapon you have to hand into play to bolster your cybersecurity and deter hackers, and that includes big data analytics.
What is big data analytics?
On the simplest level, big data analytics are the processes you use to turn big data into meaningful insights and actionable information.
Big data is defined as datasets that are high in the three “Vs” — namely velocity, volume, and variety. In other words, it’s data that floods in from internet tracking tools, Internet of Things (IoT) devices, and more.
Big data is far too large for any human being to cope with, which is why big data analytics uses forms of artificial intelligence (AI), like machine learning (ML), deep learning (DL), and natural language processing (NLP) to crunch the mass of data. Ideally, organizations gather data into cloud data warehouses, choosing between options like Redshift vs. Snowflake, to make it fully accessible to analytics tools.
How can big data analytics help with your cybersecurity?
Big data analytics tools can spot patterns in huge datasets, enabling them to reveal emerging trends and anomalies far earlier than would otherwise be possible. These capabilities can detect nascent preferences among your customers or an emerging business risk within your market, but they can also be put to work to identify nascent hacking trends or emerging cybersecurity threats.
Here are X ways that your big data analytics can help raise your cybersecurity profile.
1. Spot suspicious activity
As mentioned above, big data analytics are great at both identifying patterns and recognizing incidents that contradict those patterns, making them excellent at telling the difference between the behavior patterns of “honest” users and those of malicious actors.
Automated threat alerts pick up on potential attacks, so data scientists and IT teams can review them and shut them down before they occur. As human experts make these judgment calls between unusual but safe behaviors, and those which presage an attack, your ML analytics will learn from their decisions and grow more accurate in predicting cyber threats.
2. Strengthen your defenses
As well as helping you block potential attacks before they occur by flagging suspicious activity, the insights surfaced by your analytics can help guide your defense strategy. Automated analytics reports can reveal trends in hacking and malicious attacks, so you can prepare to defend your systems accordingly.
Insights like which kind of hacking attempts happen the most often, the geo or IP address which is the most common source of attacks, and which part of your system is targeted most often can all help you refine your protection.
3. Speed up reaction time
It’s wishful thinking to pretend that your IT systems will never be breached, and exactly what malicious actors hope you’ll do. Your organization is statistically highly likely to suffer ransomware, malware, or another type of cyber attack, so as well as doing all you can to prevent it, you also need to plan what to do once it’s occurred.
Big data analytics can detect intrusions in real-time and alert you before hackers have time to steal sensitive data or trigger their ransomware. By automating your reactions, you can stop an attack before it causes the kind of damage that makes headlines and costs you thousands of dollars to fix.
4. Understand what you’re facing
As every cybersecurity expert knows, there’s no such thing as a system that’s 100% safe. Like every other organization, your system has its vulnerabilities, and new wormholes appear constantly. Set your big data analytics to work to map your networks in real-time, so you can predict a hacker’s next moves with greater accuracy.
At the same time, your analytics tools can give insight into attacks themselves. Real-time intrusion analysis reveals the inner workings of an attack, giving you the information you need to halt this attack and lower the risks that the next one might succeed.
5. Monitor security levels
You do all you can to educate your employees about best practices for strong passwords and access credentials and to create a cybersecurity-aware culture in your organization, but everyone makes mistakes sometimes. Unfortunately, it only takes one recycled password or access credentials left on default for malicious actors to be able to march into your ecosystem through the front door.
Use your big data analytics to check up on employee security practices, so that the first person to spot that default password is you, after receiving an automated alert, and not some hacker ten thousand miles away. Some cybersecurity systems even connect with analytics to automatically respond to this kind of a notification and take steps to deal with it.
Big data analytics should be part of your cybersecurity toolkit
With their ability to crunch huge datasets and produce meaningful insights, big data analytics can help take some of the burden off stressed-out IT teams. By using analytics to spot human mistakes, suspicious behavior, and early intrusions, increasing your understanding of your vulnerabilities, and bolstering your defenses, you can lower your exposure to risk and raise your cybersecurity profile.
