Seeing Around the Corner: How Analytics Predicts Cyber Threats Before They Strike

Cyber attacks can happen in the blink of an eye. One minute your systems are functioning normally, the next minute you're scrambling to understand why users can't access files or applications are crashing. The costs add up quickly, from actual ransom demands to lost productivity and recovery efforts. Wouldn't it be great if you could predict these threats before they happen?

 

Well, with the power of analytics, you actually can. Security teams have more data at their fingertips than ever before. The key is knowing how to harness it. Analytics engines fueled by machine learning can establish baselines for normal behavior across users, devices, applications, networks and cloud services. By continually comparing emerging activities against benchmarks, intelligent algorithms can identify abnormal shifts that may indicate cyber threats are looming.

 

What Cyber Security Threats Can You Predict?

 

Advanced analytics can foreshadow a wide range of potential cyber security incidents. A few prime examples that may be detectable based on deviations from baseline norms include:

 

• Ransomware attacks: By analyzing data access patterns, you may be able to identify initial infiltration points or behaviors characteristic of ransomware. For example, a flurry of unusual file share activity late at night could suggest an unauthorized user sweeping large volumes of data into a staging area prior to encryption deployment. Recognizing these early warning signs allows you to isolate potential infections before files get locked down across connected systems.
   

• Data exfiltration attempts: If you can recognize when an unusually large amount of data is being accessed, assembled, and potentially prepared for unauthorized transfer, you can cut off the effort before critical data leaves your environment. Signs may include employees accessing file shares abnormal for their role, broken file transfer sessions being repeatedly restarted, surges in outbound data transfers at odd hours, and connections to external IP addresses never linked to your organization in the past.
   

• Brute force login attempts: Machine learning algorithms can establish benchmarks for normal login activity in terms of locations, devices, time patterns, retry frequency and more. Detecting a sudden influx of login attempts from unexpected places or at scale beyond typical patterns is an early indicator of brute force efforts to crack passwords through volume.
   

• Increasing activity from dormant malware: Analytics can reveal malicious software that may have slipped in months or years ago but suddenly receives commands from its control center to launch activity. A swarm of outbound connections, data encryption, or file destruction activity from dormant entities shows malware may have awakened.

 

The list goes on, but those examples show just how analytics engines fueled by machine learning can translate security data into predictions about impending threats. The key is knowing your organization’s normal behavior, where deviations may indicate trouble brewing, and implementing monitoring and analytics quickly enough to prevent or minimize harm.

 

How Do Analytics Predict Emerging Threats?

 

Advanced analytics engines work by ingesting massive amounts of security data from across an organization's on-prem and cloud infrastructure. This includes data on user behaviors, application activities, server workloads, network traffic and so on. For mobile security, data may come from device management systems, mobile app firewalls, and endpoint agents. Cloud security data can originate from CASB systems, cloud access logs, and cloud infrastructure monitoring.

 

Sophisticated machine learning algorithms process all this data to establish normal behavior patterns. Think of it like the algorithms learning what typical user, application and network patterns look like across the environment.

 

The algorithms are constantly analyzing new security data to compare it to those baseline patterns. Even if an individual data point seems benign on its own, the algorithms can correlate different factors that together may suggest malicious activity.

 

• Unusual spike in upload activity in the middle of the night

• Large data volumes moving to uncommon destinations

• Series of failed application login attempts

• Burst of record deletions from a database

 

For example, if there is a spike in large data uploads between 2-3 AM to an uncommon external destination, the algorithms may recognize that pattern as suspicious and indicative of someone illegally transferring data out of the environment.

 

Or the algorithms may connect the dots between a series of failed application login attempts followed by a barrage of database record deletions as signs someone has hijacked a privileged user account.

 

The algorithms get smarter over time as they incorporate new threat data and learn new ways attackers are evolving their techniques. By comparing daily security logs against the baseline patterns, the analytics engines can identify subtle deviations that human eyes would likely miss. This allows security teams to investigate and respond to potential threats before significant damage occurs.

 

Building an Analytics-Driven Defense Strategy

 

Transforming all that data into meaningful, actionable threat predictions takes an analytics platform purpose-built for security use cases. Legacy business intelligence tools can't effectively model complex cyber risk scenarios or support real-time data velocities and analysis needed to uncover emerging behaviors and anticipated incidents.

 

Here are 5 recommendations for leveraging analytics to continually assess your defense posture and risk trajectories:

 

1. Instrument critical data sources first.

 

When launching analytics, resist dumping every log source in at once. That flood of data can bog down the system. Instead, first connect the data sources monitoring your most critical assets and sensitive user accounts. Once the analytics engine has built solid behavioral profiles, introduce more data over time.

 

2. Blend machine learning with human expertise.

 

Analytics automation provides heavy lifting to detect emerging threats, but humans still need oversight. Make sure staff are reviewing model findings, validating alerts, calibrating configurations and enhancing data feeds. Let powerful AI do the constant data crunching while skilled experts provide guidance.

 

3. Establish tight baseline profiles.

 

Help your analytics engine tell normal from abnormal by feeding in historical logs to establish baselines for user, application and network patterns. That way when oddities pop up, analytics can flag them against known norms. Think of it like teaching the system what’s “clean” before expecting it to find “dirty.”

 

4. Provide visibility through reporting dashboards.

 

Build visual dashboards to let teams monitor activity levels, risk scores and threats against historical norms. Dashboards allow staff to visually slice and dice data to analyze what factors are driving threat trajectories. Taking in the full view makes emerging risks crystal clear.

 

5. Iterate models continually as behaviors evolve.

 

Hackers constantly evolve tactics to slip past defenses. Regularly feed updated threat intelligence into analytics to teach models new maneuvers. Check that risk scoring stays accurate by monitoring for alert fatigue or missed threats. Keep enhancing analytics to match adversaries’ latest tricks.

 

Final Word

 

Advanced analytics offers a powerful way to peek into the future of cyber threats hiding in your environment. By continually monitoring behavior patterns against risk benchmarks, organizations can identify budding attacks before they blossom into full-blown incidents.

 

While machines do the heavy data crunching, human expertise still guides configurations, investigates alerts and enhances defenses over time. It’s the partnership between vigilante algorithms and savvy security teams that unlocks the full potential of predictive security.