Logrhythm machine learning and artificial intelligence in cybersecurity 2017
The future of artificial intelligence
The next stage for AI security will be a shift to the cloud. Hosted
systems will offer more scalability – unlike on-premise systems
that can quickly become overloaded when searching for suspect
behaviour further back into logs, cloud-based systems will be able
to inspect a data lake and quickly provide results.
Using the cloud will also hugely increase the data available to
AI systems and accelerate their learning rates. It will allow the
creation of peer-based profiling – instead of looking at typical
behaviour within an organisation, AI will be able to compare an
organisation with its regional peers, such as similar companies or
even people performing comparable jobs in other industries. With
the wealth of information available, AI systems will help companies
create better, more effective detection rules customised to their
organisation and industry.
UK businesses
deal with over
5,000 security
incidents
every year[2]
And, as AI’s capabilities build, it will increasingly free up security
analysts to focus on other priorities, including staying ahead of the
curve on attacks, monitoring new and emerging hacker groups, and
keeping their skills and knowledge up to date.
What’s normal for your organisation?
Security systems can easily find anomalous behaviour on a network but
every organisation is different. What’s suspicious for one organisation may
be the standard order of business for another.
That’s where AI really proves its worth: artificial intelligence can learn
to recognise the unique way that users, hosts, applications and devices
behave on an organisation’s network, and so flag up when any activity is
outside what would be expected.
By combining AI with pattern recognition software and whitelisting,
AI can help detect security incidents without creating unnecessary false
positive or negatives.
How can you use AI for security? One solution is LogRhythm AI Engine, which
delivers real-time visibility to risks, threats and critical operations issues.
In the longer term, AI will allow security teams to take a more
strategic and proactive approach to cybersecurity instead of just
reacting to the latest attack.
Machine learning and artificial intelligence in cybersecurity The next level in security analytics
01628 918300 | [email protected] | www.logrhythm.com
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As threats grow more sophisticated it is becoming ever more
important for security teams to have their time and skills move away
from reacting to the last attack, and instead look forward, taking
steps to defend against the next threat on the horizon.
At LogRhythm, our mission is to help organisations detect,
respond to, and neutralise threats using the best technology tools
available.
To find out more about how AI and machine learning can help
improve your security profile, contact us.
About LogRhythm
LogRhythm is the pioneer in Threat Lifecycle Management™ (TLM)
technology, empowering organisations on six continents to rapidly detect,
respond to and neutralise damaging cyberthreats. LogRhythm’s TLM
platform unifies leading-edge data lake technology, artificial intelligence,
security analytics and security automation and orchestration in a single
end-to-end solution. LogRhythm serves as the foundation for the AIenabled security operations centre, helping customers secure their cloud,
physical and virtual infrastructures for both IT and OT environments.
Among other accolades, LogRhythm is positioned as a Leader in
Gartner’s SIEM Magic Quadrant.
www.logrhythm.com
[1] 2017 Global Information Security Workforce Study: https://iamcybersafe.org/wp-content/uploads/2017/06/Europe-GISWS-Report.pdf
[2] PwC Global State of Information Security Survey 2017: https://www.pwc.co.uk/issues/cyber-security-data-privacy/insights/global-state-of-information-security-survey-2017.html
Machine learning and artificial intelligence in cybersecurity The next level in security analytics
01628 918300 | [email protected] | www.logrhythm.com
| 4
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