How AI-Driven Hyperautomation is Transforming Cybersecurity
When security analysts work to identify cyber threats, security data is their window into the wider enterprise network. Whether it’s files, network packets, or logs – all traces need to be monitored and acted upon in near-instantaneous time. AI-driven Hyperautomation stands as the new forefront in cybersecurity: defined by Gartner as the use of automation within all business processes that need to be automated, it promises to give lean teams the tools to manage the entire security pipeline – from raw data to threat analysis, incident remediation, and beyond.
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Automating The Three Pillars of Cybersecurity
The vast amounts of data being generated across an enterprise’s network is too much for simple manual tracking. Across data collection, analysis, and threat remediation, let’s define each field’s automation maturity levels – and how Stellar Cyber is pushing for peak maturity within AI-driven hyperautomation.
Data Collection Automation
Closest to the individual devices, network hardware, and applications that make up an enterprise’s productivity stack, the collection and monitoring of raw data determines an enterprise’s true visibility. There are two key types of raw data used to monitor enterprise health: logs, and network activity.
Log collection
The bread and butter of cybersecurity monitoring, logs are records of events created by applications, networking devices, and servers.
At the most basic level of maturity, logs are included in the cybersecurity analyst process via log replication – where an analyst manually sets up a local script on a server or device that periodically replicates all logs and deposits them into a central repository. Used mainly for batches of logs, each log is often formatted to be human-readable – and often only read when analysts are manually trying to resolve an issue, or exploring how a security incident began.
At the medium level of automation maturity, this process begins to incorporate real-time visibility by automatically pulling logs to a central management system, usually via an API or deeper application configuration. The logs’ individual formatting also becomes more machine-centric, with a greater emphasis on structured layouts that can easily be ingested by log management tools. Analysts still need to manually aid these tools in selecting which devices to include, and often need to go back to sample and adjust their log management practices over time.
Finally, log ingestion at its most automated goes beyond a pure collection system to incorporate automatic device discovery. Whether it’s through API, log sources, or native sensors, every enterprise device is able to be discovered and tracked, regardless of its activity on the network.
At the most basic level of maturity, logs are included in the cybersecurity analyst process via log replication – where an analyst manually sets up a local script on a server or device that periodically replicates all logs and deposits them into a central repository. Used mainly for batches of logs, each log is often formatted to be human-readable – and often only read when analysts are manually trying to resolve an issue, or exploring how a security incident began.
At the medium level of automation maturity, this process begins to incorporate real-time visibility by automatically pulling logs to a central management system, usually via an API or deeper application configuration. The logs’ individual formatting also becomes more machine-centric, with a greater emphasis on structured layouts that can easily be ingested by log management tools. Analysts still need to manually aid these tools in selecting which devices to include, and often need to go back to sample and adjust their log management practices over time.
Finally, log ingestion at its most automated goes beyond a pure collection system to incorporate automatic device discovery. Whether it’s through API, log sources, or native sensors, every enterprise device is able to be discovered and tracked, regardless of its activity on the network.
Network Security Monitoring
Network security monitoring takes a step back from the individual actions within the application, and instead looks at the traffic flowing across an enterprise network to assess malicious actions.
Non-AI approaches to network security monitoring have worked well in the past, but cybercriminals have rapidly adapted their approaches around them. Older security tools simply compare network packet information against a premade list of known strategies – and old firewalls struggle to contend with today’s end-to-end encrypted traffic.
Automated network security tools can gather intel from far vaster swathes of networks, across both public and private cloud and on-prem hardware alike. Stellar Cyber’s network sensors dig deep, collecting metadata across all physical and virtual switches. Its sensors decode payloads via Deep Packet Inspection, and can operate on Windows 98 servers and up, alongside Ubuntu, Debian, and Red Hat.
Gathering all this data may be foundational to solid cybersecurity – but it still needs to be turned into insight and, critically, action.
Non-AI approaches to network security monitoring have worked well in the past, but cybercriminals have rapidly adapted their approaches around them. Older security tools simply compare network packet information against a premade list of known strategies – and old firewalls struggle to contend with today’s end-to-end encrypted traffic.
Automated network security tools can gather intel from far vaster swathes of networks, across both public and private cloud and on-prem hardware alike. Stellar Cyber’s network sensors dig deep, collecting metadata across all physical and virtual switches. Its sensors decode payloads via Deep Packet Inspection, and can operate on Windows 98 servers and up, alongside Ubuntu, Debian, and Red Hat.
Gathering all this data may be foundational to solid cybersecurity – but it still needs to be turned into insight and, critically, action.
Data Analysis Automation
There’s a degree of data analysis that will always require the expertise and knowledge of a real human. However, the advances in automated analytics now allow analysts to make time-critical decisions with greater clarity than ever before.
Event analysis at an early stage of automation often relies on an analyst having to connect the dots themselves – whether it’s a software version that needs patching or an overseen flaw. In the worst-case scenario, the attacker is aware of – and actively exploiting – the flaw before an analyst is even aware of it. While it’s still manual, collating all of the different data formats into a central dashboard is the foundation of the now-ubiquitous Security Information and Event Management (SIEM) tool.
Around a decade ago, one of the capabilities boasted by highly experienced security professionals – the ability to recognize an attack they’ve witnessed before – could suddenly be wielded by newer teams thanks to signature-based detection. Thus organizations began to benefit from a medium level of automated analysis. If a file signature or IP address matched a previously tagged attack, an analyst could be alerted immediately (usually via their SIEM tool).
However, this basic form of event analysis still had essentially no answer to zero-day or novel attacks. Furthermore, analysts faced an even bigger challenge: security events were being generated far faster than they could be processed.
Event analysis at an early stage of automation often relies on an analyst having to connect the dots themselves – whether it’s a software version that needs patching or an overseen flaw. In the worst-case scenario, the attacker is aware of – and actively exploiting – the flaw before an analyst is even aware of it. While it’s still manual, collating all of the different data formats into a central dashboard is the foundation of the now-ubiquitous Security Information and Event Management (SIEM) tool.
Around a decade ago, one of the capabilities boasted by highly experienced security professionals – the ability to recognize an attack they’ve witnessed before – could suddenly be wielded by newer teams thanks to signature-based detection. Thus organizations began to benefit from a medium level of automated analysis. If a file signature or IP address matched a previously tagged attack, an analyst could be alerted immediately (usually via their SIEM tool).
However, this basic form of event analysis still had essentially no answer to zero-day or novel attacks. Furthermore, analysts faced an even bigger challenge: security events were being generated far faster than they could be processed.
You’re (Probably) Already Familiar with Automated Analysis
Machine learning takes copious quantities of logs and network events, and runs them via an algorithm, which then learns their individual patterns. This is the foundation of behavioral monitoring – when run over long periods of time, it becomes possible for algorithms to build a benchmark for typical device behavior. For instance, if a user usually spends their working day editing documents and messaging colleagues over Teams, behavioral analytics engines (like the one powering Stellar Cyber) are able to alert analysts when a user account suddenly begins accessing lots of different files at a completely unexpected time of day. Analysts can sort users according to their risk score, allowing for rapid discovery.
While anomaly-based behavioral analytics can predict and therefore prevent attacks, it can be prone to false positives and cluttering up incident response workflows – which is where the final layer of security automation is making the biggest change today.
While anomaly-based behavioral analytics can predict and therefore prevent attacks, it can be prone to false positives and cluttering up incident response workflows – which is where the final layer of security automation is making the biggest change today.
Incident Response Automation
The last two steps – data collection and analysis – both lead to one thing: incident response.
Incident response that relies on a basic level of automation requires the analyst to manually disable network access when quarantining malware-infected devices, remotely install new software patches, and reset passwords and usernames for users that may have had their accounts breached. You may notice these are primarily reactive in nature – this is a result of manual intervention’s snail-like pace.
Progressing into a mid-tier level of incident response automation, this takes the foundation of behavioral analytics and acts accordingly – often by automatically denying suspicious users access to critical resources, or alerting the correct analyst according to their area of expertise. Playbooks allow for security teams to maintain full control over automatic responses, letting an AI-powered tool excel in performing the repeatable mundane tasks of day-to-day cybersecurity.
This level of incident automation is keenly susceptible to one issue, however: false positives. These can wrongly place restrictions on a user or device, severely impacting productivity. Companies with mature incident response pipelines are already threshing out high-accuracy incident response process: it’s via hyperautomation.
Incident response that relies on a basic level of automation requires the analyst to manually disable network access when quarantining malware-infected devices, remotely install new software patches, and reset passwords and usernames for users that may have had their accounts breached. You may notice these are primarily reactive in nature – this is a result of manual intervention’s snail-like pace.
Progressing into a mid-tier level of incident response automation, this takes the foundation of behavioral analytics and acts accordingly – often by automatically denying suspicious users access to critical resources, or alerting the correct analyst according to their area of expertise. Playbooks allow for security teams to maintain full control over automatic responses, letting an AI-powered tool excel in performing the repeatable mundane tasks of day-to-day cybersecurity.
This level of incident automation is keenly susceptible to one issue, however: false positives. These can wrongly place restrictions on a user or device, severely impacting productivity. Companies with mature incident response pipelines are already threshing out high-accuracy incident response process: it’s via hyperautomation.
How Stellar Cyber’s Hyper Automation is Transforming Incident Response
Back in the intro, we explained how hyperautomation is the process of stacking layers of automation to produce the best possible business outcome. In mature security stacks, hyperautomation combines the in-depth, pattern-based analysis of machine learning algorithms, with the process of incident contextualization.
Stellar Cyber’s Graph ML is able to map the correlations between individual anomaly alerts, and craft them into cases: converting thousands of alerts into the few hundred true events they may be part of. Each case is then automatically enriched and prioritized, according to the unique qualities of its individual alerts. Finally, analysts are presented with a single point of reference – a dashboard that collates the entirety of their organization’s behaviors, flaws, and devices into streamlined cases.
If your organization isn’t yet at peak automation maturity, don’t worry – it’s normal for automation maturity to progress sporadically, as tooling is upgraded every few years. If you’re curious how Stellar Cyber offers the most cost-effective Open XDR platform on the market, reach out for a demo today.
