Machine Learning (ML) and the role that it plays in cybersecurity is ever-evolving – not unlike the growing security threats that target endpoints across all computing platforms.
The driving factor for incorporating machine learning into security solutions is the benefits of analyzing data points to improve an organization’s security posture at a performance level that human intervention simply cannot compete with. ML technologies aren’t merely powerful but capable of analyzing enormous quantities of risk data sets in rapid order to:
- Identify a variety of security threats – both known and unknown
- Improve capabilities through continuous learning
- Detect potentially new attacks through historical and empirical data
- Provide a faster, more efficient means of threat response
- Automate remediation workflows to effectively resolve threats
In the case of Jamf Threat Defense, for example, the threat intelligence engine used to identify and prevent known threats is called MI:RIAM. Leveraging powerful, advanced machine learning, MI:RIAM detects and prevents unknown threats as well. Through consistent scanning and data analysis, MI:RIAM is capable of blocking zero-day threats, such as those presented from phishing websites and malicious domains in real-time – before your devices are impacted – among other safeguards to keep your endpoints secure.
What role does machine learning play in endpoint security?
According to IBM, technologies like ML “provide rapid insights to cut through the noise of daily alerts, drastically reducing response times.” The speed and efficiency at which ML aids under-resourced security teams deal with the growing volume of cybersecurity threats and attack complexities, helping them to stay ahead of threats by curating threat intelligence from a number of different sources to develop dynamic threat intelligence reports, tailored to meet the needs of their organization.
What are the benefits of ML in cybersecurity?
In simple terms, ML helps IT and Security teams to “connect the dots” between threats and vulnerabilities, threading together all data points to develop a holistic, top-down look at where your organization stands within the context of its security posture and ability to keep devices, users and data safeguarded against evolving security threats.
7% of work devices continued to access cloud storage services after being compromised in 2021.
Rise of the Machines
The ability to detect new threats, including potentially malicious activity, is one of the biggest draws to implementing ML within your cybersecurity protection plan. Whereas traditional security solutions depend on signatures of known threats to prevent them from compromising endpoints, ML leverages sophisticated algorithms to detect malware, phishing attempts and other attacks through predictive intelligence, based on the analysis of a potential attack’s behavior and how it compares to known attack patterns. ML works to detect even the slightest semblance of the next-generation form of attack before it has a chance to compromise your devices or breach your network.
Speaking of abilities, ML has the ability to, well, learn – or be taught – further enhancing their capabilities to offer greater protection by identifying more threats and preventing security issues from impacting systems.
For example, they possess the ability to:
- Be trained to detect highly sophisticated attack types
- Perform high-level pattern recognition
- Utilize natural language processing to enhance protections
- Scrape articles, technical papers and studies to curate data
- Analyze anomalies and attack heuristics to determine trends
- Catalog data to further enhance and share knowledge globally
- Prioritize decisions made to best protect endpoints based on formulations
- Offer holistic protection by providing granular protections that are industry-specific
Part in parcel with its ability to find threats faster, IBM further sums up how ML reasoning works to not only “understand cybersecurity threats and cyber risk by consuming billions of data artifacts,” but also to perform analysis on data consumed to determine what relationships exist between a multitude of threat vectors. This analysis allows it to paint a comprehensive picture of the overall risk posed to organizations in mere seconds or minutes, thanks in no small part to the automated nature of machine learning.
Consider potentially malicious threats, such as unwanted files, communications from unknown IP addresses or domains and suspicious behaviors being performed by users that have already been granted access to organizational resources. Taken at face value, none of these are guaranteed to be a threat, as they could simply be part of a larger session that is occurring with a new customer relationship or employee within the company.
In 2021, 5% of devices, or 20% of organizations, were impacted by risky device configurations.
No, this isn't the AI from the Terminator series that seeks to protect humanity from itself by effectively putting an end to the human race. But it's similar in concept enough that when considering the examples of potentially malicious threats in the previous section, ML has the power to review all the individual, seemingly unrelated "loose threads" simultaneously to determine if indeed there is cyber risk involved, to what degree, on which devices, how applications and/or services are being targeted and what the potential fallout may be – the resultant effect of all these detailed examinations leads to effectively shutting threats down before a data breach can even occur.
This is in stark opposition to the alternative, if manually analyzed, where any connections between unknown or suspect actions could take hours to fully examine, could be missed as part of general human error or maybe dismissed altogether given the difficulty involved in properly inspecting each piece of data.
How MI:RIAM works
Before we dive into what makes MI:RIAM tick, let’s explain the meaning behind the name.
MI:RIAM stands for Machine Intelligence: Real-time Insights and Analytics Machine and as we’ve discussed, MI:RIAM is the advanced machine learning technology behind Jamf Threat Defense. More specifically, this component is responsible for performing the following tasks to keep your device fleet, users and sensitive data safe and secure from risk and security threats:
- Discovering zero-day attacks, such as phishing attempts
- Identifying numerous attack types, like cryptojacking and C2
- Automatically blocking sophisticated attacks on the network to prevent loss of sensitive and critical data, such as data exfiltration
- Providing protection for apps and services, including web browsers, email, social media and SMS
- Endpoint defense for mobile workers that performs device health checks to prevent breaches from occurring due to vulnerable configurations or risky connections
- Preventing known malware and malicious software from compromising devices
- Performing app insights with vetting workflows to create detailed threat intelligence reports, including listing permissions and embedded URLs that put data at risk
- Working alone or alongside first- and third-party tools to allow enforcement of security policies while tailoring protections on the fly
- Automated remediation of endpoint threats based on comprehensive risk assessments and performed in real-time
- Adaptive access to applications and services by trusted devices occurs through continuous monitoring of broad telemetry and contextual input sets
36% of organizations encountered malicious network traffic indicators on a remote device in 2021.
You’re never alone in protecting your organization from cyber threats when you have MI:RIAM at your side.
Jamf Threat Defense, powered by ML and advanced learning deliver cloud-based security that operates on your devices and in your network for the ultimate in cybersecurity protection – on- and off-premises.
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