The Benefits of Data Lakes and Predictive Analytics for Cybersecurity

It is a digital battlefield out there. Cyber threats are becoming more sophisticated and cybersecurity needs to be proactive rather than reactive. Think of cyber defense as a security radar that keeps scanning the horizon and annihilates the enemy as soon as they are just a blip on the screen.

This is no longer the work of science fiction. Robots might not be ready to take over our world, but the threat of cybercriminals is very much here. New cybersecurity measures are a combination of Security Data Lakes, Predictive Analytics and Machine Learning.

Security Data Lakes

With data streaming in from many devices and sources, cybersecurity tools need to be looking everywhere to detect possible cyber attacks. Time is of the essence. Blocking a known enemy may be possible. But how about when you don’t know what form a new threat will take? This is why cybersecurity analysts and data scientists need to spend more time analyzing data to plug threats and vulnerabilities rather than spending time integrating data from different sources.

A Security Data Lake is a specialized data lake that is designed to ingest log files from all sources. In a legacy management system, logs from different security products like firewalls, VPN, DLP, proxies, web servers etc. from different network devices were stored in different data silos. Cyber analysts would spend more of their time accessing these different databases. For the first time, Security Data Lakes allow all log data to be ingested, visualized and analyzed from one place. Additionally, Security Data Lakes allow for the enrichment of security data with additional information to provide necessary context. This context can be extremely helpful in preventing false alarms. With log volume now being in petabytes, Security Data Lakes are perfectly suited for data growth.

Many industries, including banking, airline and insurance, are turning to Commercial Security Data Lakes to detect intrusion behavior faster. This is because these commercial products perform a lot of the processing automatically. They have built-in parsers that will format data according to type and recency. This is important to enable fast search of dense security logs and allow security analysts to quickly get to the information they need.

Predictive Analytics for Cybersecurity

Hackers are also using Machine Learning and AI to identify and target specific weaknesses. They will stealthily burrow in and sneakily infiltrate your systems. By the time you detect their presence, it is just too late. It is now the age of organized cybercrime. Unless you can identify these patterns and detect even the smallest changes in real-time, you will not be able to close the gates before data is stolen.

Identifying cybercrime modus operandi and determining where they can hit next is what Predictive Analytics is all about. It will crunch huge volumes of data, identify patterns and sniff out the faintest anomalies. In fact, Deep Learning models can understand relationships between multiple events and can pinpoint behaviors that are indicative of different stages in a kill chain process.

Natural Language Processing is also a big benefit to cybersecurity defense. Threats can come from anywhere and NLP engines can process data irrespective of the language, something beyond the ability of a human. Data can be harvested from anywhere based on preset rules like company name and technical indicators. It can pick up information from a webpage or a social media page, calculate risk to an organization and make predictions on the probability of attack.

It is estimated that loss due to cybercrime will be a whopping 6 trillion dollars by 2021. Some businesses may never recover from a major data breach due to the cost and loss of reputation and trust in the company. It’s no wonder, then, that Gartner forecasts $124 billion will be spent on security products and services in 2019 alone.

What the future holds for cybersecurity

With more and more technology moving to the Cloud, this transition brings with it greater risk. Mid-sized companies are already at a higher risk of botnet infections with their lower security cover compared to larger enterprises. Cyber hackers will find it easier to go after them. Every business will need to take a zero-trust security approach since the hyperconnected networks in the Cloud need only one breach for all the network to be compromised.

The future of security technology may involve Blockchain. Blockchain provides highly advanced encryption and can eliminate the need for passwords. As more and more devices get plugged into IoT, security will become important at an individual level as well. This is already seen in the Healthcare environment where insulin pumps and pacemakers have a wireless interface. Any mistake can lead to life-threatening situations. Virtual assistants at home and Electronic Control Units of automobiles can leave us exposed to cyber threats. AI, along with Machine Learning, will be woven into all security systems to provide a higher level of defense.

Data Lakes and Predictive Analytics provide a new line of defense for cybersecurity. If your business is looking for more guidance on cybersecurity – especially as it pertains to data storage – reach out to the Sertics team today. We can help you create a robust, organized Data Lake that follows all of today’s top security protocols while uncovering deeper business insights with easy-to-understand visualizations and more.

Venkatesh Kalluru has more than two decades of experience spearheading agile development projects for firms including ReThink IT, GCE, and AT&T. Venkatesh has expertise in a diverse range of technologies including Predictive Analytics, Machine Learning, AI, IoT and more. Venkatesh studied computer science at Jawaharlal Nehru Technological University in Hyderabad, India, and earned his master's degree in computer science from George Mason University in Virginia.