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 | 3 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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