The Massive Data Breach – Reducing “Dwell Time” and the Resulting Damage
What happens when a major company is faced with a massive data breach? After the data breach has been discovered, investigators often find out that the hackers have been occupying their network for days, if not months, and sometimes years. This is known as dwell time. In a SANS survey, 20 percent reported dwell times of a month or longer.
This week we’ve seen what can happen when a major company like Facebook—or any organization for that matter—is faced with a massive data breach. They face possible loss of intellectual property and brand damage if they expose the privacy of their customers, partners, and workforce.
And now with GDPR and California’s SB1386 requiring notifications so that victims can protect themselves from identity theft and disclosures of their personal and financial data, they can lose the trust of their customers, and there can be staggering fines in the billions.
After a data breach has been discovered, investigators often find out that the hackers have been occupying their network for days, if not months—and sometimes years.
For about 50 percent of cybersecurity respondents, a 2017 SANS Institute survey found the average time between an initial compromise and its detection—known as dwell time—is over 24 hours. Twenty percent reported dwell time of a month or longer.
Big data comes with greater cybersecurity risks
Large organizations are usually responsible for massive amounts of data—sometimes involving the private information of billions of people. The larger the organization and the more customers it has, the more logs and events it has—generated by everything from cloud services (such as AWS and Azure) and SaaS applications to traditional network and on-premises data sources.
It’s nearly impossible for security analysts to manually prioritize and shift through huge amounts of log data to find a security breach.
All of that data gives attackers more places to hide. For example, hackers can enter a network through a less sensitive, and thus less monitored vector such as an unprotected cloud server, an IoT device, or a shared employee laptop. They can then move laterally from that single device to access critical resources spread across the organization.
Anomaly detection is your best defense against dwell time
Frequently an intrusion is detected by a notable change, such as a rapid increase in network traffic, a suspicious system login location or time, or the unusual export of sensitive information such as with data exfiltration. But not all attacks have an obvious pattern.
Often hackers who have gained access to your network are conducting a “low and slow” attack. This is where they carefully and methodically move laterally across devices and users so as not to attract attention—doing reconnaissance and strategizing on how best to exfiltrate your data.
Correlation rules—often used in cybersecurity—can only go so far in catching malicious activity. Frequently they don’t successfully uncover an attack because they lack context, or miss new hacks that have yet to be discovered and defined.
Such rules can also generate many false positives that overwhelm security teams; they often correlate incidents when a true security risk doesn’t exist. For example, a false positive alert may occur when a vacationing employee logs in to their corporate account from a country known as a cyberattack source, and analysts can mistakenly assume they are an attacker. For reasons like this, correlation rules require constant maintenance by your security teams.
With machine learning, what took weeks to investigate can be done in seconds
Machine learning on the other hand makes it faster to find anomalous and suspicious user and device behavior. Its algorithms can baseline normal behavior in your network environment, then alert your security team whenever anomalous activity occurs.
Prebuilt security incident timelines can display the full scope and context of related event details. This means that analysts no longer have to comb through massive amounts of raw logs to manually create a timeline as part of any investigation.
As a result, analysts can detect breaches sooner and reduce the amount of time that attackers are “dwelling” in a network environment, significantly reducing the size of a breach and its devastating impacts.
These attacks can be personal. We all know that mega breaches can have major impacts and repercussions with hackers accessing personal information like birthdays, family names, hometown, and our personal preferences and political affiliations. Moreover, as FTC Commissioner Rohit Chopra recently stated, “Breaches don’t just violate our privacy. They create enormous risks for our economy and national security.”
With the increasing sophistication and worsening impacts of mega data breaches, now is the time for organizations to implement smarter security management solutions.
Click here to learn more about how Exabeam Advanced Analytics and Smart Timelines can help you reduce the dwell time of a security breach.