Getting the benefits of AI threat detection tools is becoming less of an option for security operation centres (SOCs).
Last year, the UK experienced more cyber attacks than any other country in Europe. According to IBM’s X-Force Threat Intelligence Index report, nearly half (43%) of all cyber attacks in Europe targeted UK-based organisations.
The threat level may be rising exponentially, but the truth is that the average SOC doesn’t have the bandwidth or the capabilities to detect threats before they become full-blown cyber attacks.
In a recent ESG survey of security professionals:
As the cyber threat landscape darkens, SOCs urgently need to find a way to fill this cybersecurity gap.
Enter AI threat detection.
Threat detection was one of the earliest use cases of artificial intelligence (AI) in cybersecurity.
Whereas traditional threat detection processes rely on human analysts to analyse data patterns and behaviours (something that is getting harder due to the high noise-to-signal ratio), AI-driven threat detection platforms can detect and isolate threats, including zero-day malware, in real-time with higher accuracy and without human analyst input.
AI threat detection solutions work by establishing a behavioural baseline and continuously monitoring an organisation’s environment for events that fall outside this baseline threshold.
When a suspicious event is spotted, AI detection tools evaluate and correlate data to determine whether the event is truly malicious before issuing a notification for human analyst attention or taking an automated containment/remediation action.
AI-based threat detection and response tools can bring down the number of false positives plaguing SOC teams, improve the accuracy and speed with which security professionals can identify and remediate cyber threats, and reduce employee attrition.
In other words, AI threat detection can reduce the risk that an organisation will fall victim to a cyber attack.
But what does that mean in terms of security ROI? A recent report by IBM on automation and AI technologies for security operations dug into the numbers to find out.
The report, which surveyed 1,000 IT and operational technology (OT) cybersecurity leaders from 16 industries and 5 global regions, found that organisations which adopted AI and automation for security:
A lot of these benefits come as a result of faster threat detection and response. For instance, take an organisation that does not use AI and takes 230 days to identify, detect, and remediate an attack. If this organisation were to use AI, IBM reckons that it could reduce this time to 99 days.
The faster SOC teams can discover threats in their environments, the faster they can remediate them. This can lead to a significant reduction or even total avoidance of associated reputational and operational costs.
SenseOn, a unified cyber threat detection and response security solution, uses machine learning AI to automatically detect and respond to threats across an organisation’s entire digital attack surface.
Here’s what makes our AI platform stand out:
Without great data, no AI-powered system can produce accurate results.
One of the biggest issues facing organisations today is security data. Not only is there too much of it, but it is also often of poor quality. Because siloed security tools like endpoint detection and response (EDR) produce different information in different formats, pulling this data together and making correlations between alerts is a massive challenge for the average SOC.
SenseOn solves this problem using a “Universal Sensor,” a low-impact software program that can be deployed across a company’s devices, databases, servers, and cloud environments.
This allows SenseOn to capture high-quality data from every layer within an organisation’s environment, including user, device, network, and process telemetry, down to deep packet inspection.
Thanks to our Universal Sensor, security teams have complete, granular visibility into their environments, and SenseOn can get an accurate picture of how an organisation’s estate works over time.
Rather than looking for signs of an attack within an organisation’s network, SenseOn examines everything that is not normal behaviour for a particular company. When it notes instances of user and device actions that do not conform to the established baseline,
SenseOn treats it as a suspicious anomaly.
Importantly, SenseOn is self-learning, with established baselines continuously adapting to changes in an organisation’s environment.
SenseOn does not flag anomalies as soon as it identifies them. Instead, it notes an anomaly as an “Observation.” It then looks at data from other sources on that observation, both in isolation and in combination with other observations and data points, to see if there’s a link between them.
Observations that turn out to be benign are noted in the platform and can be referred back to by security analysts in their own time. This greatly reduces the number of false positives.
Observations that turn out to be genuine become a “Case.” These are either escalated for human analyst attention based on their severity or, when they’re time sensitive (i.e., ransomware), the SenseOn platform can take automated incident response action and isolate infected devices to prevent the threat from spreading.
All Cases are also mapped to the MITRE ATT&CK framework, giving analysts enough context and next steps, and can be sent to a company’s own SIEM via a custom API integration. SenseOn can also integrate with workflow tools like Slack and Jira for a better user experience.
With threats becoming more sophisticated, more security leaders are turning to AI threat detection tools to aid them and their teams in their threat detection and response efforts.
Ready to give SenseOn a go? Try our demo today.