In today’s highly complex digital world, rapidly identifying and responding to a data breach is critical to minimize its impact.
Furthermore, with mobile technologies and the Internet of Things (IoT) collecting vast amounts of data and playing an increasingly important role in modern business, protecting this information is crucial to remaining relevant.
However, to detect and immediately respond to attacks, security teams need to be equipped with the right tools and processes to effectively correlate enormous amounts of real-time and historical security event data.
Achieving this in a real-world setting is easier said than done because of the sheer volume of data (that’s constantly generated) and the variety of attack vectors. This is evidenced in Verizon’s 2017 Data Breach Investigations Report which found that bad actors used a mix of the following:
- 51% of hackers leveraged malware
- 43% of bad actors used social attacks
- 62% of attacks were related to hacking
- 14% of attacks took advantage of human error
The global average cost of an enterprise data breach is estimated to cost as much as $3.6 million (or $141 per data record). However, in the United States, the cost of a data breach is significantly higher at $7.3 million.
This is why big data analytics professionals have stepped up and built preventative technologies, detection and response services, and effective management systems to help improve cybersecurity.
So how does big data and analytics help enterprises enhance cybersecurity? Let’s take a look.
Big Data Analytics Enhances Intrusion Detection Systems
Enterprises have already taken security measures like data encryption, firewalls, and multi-factor authentication. They have also gone on to add intrusion detection systems to monitor all traffic on enterprise networks, including some segments that may have traffic that’s generated with malicious intent.
In this scenario, big data analytics will be vital to monitor the enterprise network and detect intrusions. Furthermore, it will also help the business better understand what’s going on to make informed decisions before deployment.
However, to make proper use of intrusion detection systems with big data analytics, all systems connected to the internet should be monitored.
Machine Learning (ML) Enables Better Detection of Security Threats
When you combine both ML and data analytics, you can significantly benefit from deep analysis that can help make better predictions. In other words, it can help the business develop baselines that are based on statistical data to figure out what’s normal and what isn’t.
With artificial intelligence (AI) and statistical and predictive models, security teams can be quickly alerted when there’s a deviation from the norm.
At the same time, it’s also important for companies to embrace AI as bad actors are already using it within their own systems to identify system vulnerabilities, scale attacks rapidly, mutate ransomware in real-time, and engage in phishing attacks.
This means that enterprises will need to always stay a step ahead and the only way to effectively do that will be to take advantage of big data analytics.
Big Data Analytics Helps Improve Employee Monitoring
While the integrity of the systems can be better protected through limited (authorized) access to sensitive information, it’s simply not enough to keep enterprise networks secure. Employee monitoring will be necessary for businesses to effectively and rapidly respond to human errors.
The only way to achieve this is to leverage both big data analytics and ML. This is because AI can quickly identify anomalies in the system by quickly going through massive datasets.
This technology can also be used to detect anomalous behavior patterns that weren’t flagged by predefined security rules. AI also comes with the added benefit of prioritizing security alerts and automating responses to take the pressure off security teams.
Big data analytics in enterprise cybersecurity is critical to improving detection and response times. As a result, you can expect big data and analytics to be at the core of securing enterprise networks from now on.