Machine learning has significantly impacted the cybersecurity industry, enhancing automation in security operations through behavior-based anomaly detection. It involves supervised and unsupervised learning, serving as a critical tool for incident response and threat identification, despite its potential misuse by threat actors. Ultimately, while machine learning should not be viewed as a standalone solution, it offers substantial advantages when effectively integrated into security practices.

1. Security Automation System
Machine Learning Means For Security Operations

2. Introduction
Over the past two years machine learning has found its place firmly in
the cybersecurity industry and its benefits are indisputable. Through
machine learning, we’ve seen great improvements implemented into
technology that can make tangible improvements to our cybersecurity
posture

3. Machine Learning

4. CyberSecurity Machine Learning
Cybersecurity marketers have also gotten hold of machine learning
and it has become the buzzword du jour in many respects. When
you're able to cut through the clutter, you will find that machine
learning is more than just a buzzword and we should work to fully
understand its benefits without overly relying on it as a silver bullet.

5. What is Machine Learning?
Many people reference machine learning and artificial intelligence as if
they are the same thing, when in reality they’re slightly different.
Machine learning is a subset of artificial intelligence that focuses on
computers having the ability to learn and predict outputs based on
algorithms and statistics without being directly programmed to do so.
One of the many ways this is used in cybersecurity is for the security
automation of behavior-based anomalies

6. Machine Learning Types
Machine learning comes in two flavors - supervised and unsupervised
learning. With supervised learning, the system is fed data sets to learn
from so it can make intelligent decisions in the future, such as
identifying malicious activity. With unsupervised learning, a system
uses configured algorithms to understand what’s normal and alerts on
behavior that changes or deviates from the norm.

7. Security Analysts For Machine Learning
Security operations teams who will get the most out of machine
learning are those who take a layered approach of good leadership
guiding trained engineers who are enabled with efficient tools and
proper governance. Machine learning fills a few of these criteria, but by
itself it’s just a tool. What makes all the difference is putting these tools
in the right hands to help cyber incident response that would have
never been seen without it to enable deeper insight and analysis.

8. Threats on Machine Learning

9. Threat Actors Dig Machine Learning Too
Over time, we've seen how quickly attackers have been able to easily
bypass signature-based technology with evasive techniques. For a brief
period, early white hat adopters of machine learning helped shift the
playing field slightly in favor of the good guys. However, this didn’t
last for long and attackers were quick to respond to the shift by
attacking different vectors or implementing machine learning into
their own techniques.

10. Machine Learning for Prevention and
Detection
The ability to continually and dynamically learn what’s “normal” in
behavior, traffic patterns and usage across an organization's
environment helps machine learning-enabled tools to be more effective
in finding and preventing new attacks. For security operations
practitioners, this makes machine learning an important ally in the
identification of threats and the proactive blocking of known bad
activity so more focus can be placed on investigation and incident
response.

11. Machine Learning for Incident Response
With machine learning, millions of variables and data points can be
analyzed automatically to pinpoint anomalies that could be indicators
of compromise. By ingesting threat intelligence and using a
combination of both supervised and unsupervised learning security
operations teams can use machine learning to make meaningful
improvements to incident response programs.

12. Machine Learning for SOC Management
Machine learning can enable your SOC management systems to get
smarter about who on your team is best for handling a particular type
of threat and automatically assign that analyst when the next case
arises.

13. Conclusion
While you should always be wary of cybersecurity buzzwords, machine
learning truly does have tremendous promise for security operations
teams. The technology is giving SOC teams a leg up in many areas,
including predictive and behavioral analysis, and it will continually
change the ways we add visibility into our networks and systems,
conduct investigations, respond to incidents and manage security
operations.