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October 10, 2021

AI Uncovered Outlaw's Crypto Mining Operation

Discover how Darktrace AI technology exposed a hidden cryptocurrency mining scheme. Learn about the power of Darktrace AI in cybersecurity.
Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Oakley Cox
Director of Product
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10
Oct 2021

Infamy is a paradoxical calling for cyber-criminals. While for some, bragging rights are a motivation for cyber-crime in and of themselves, notoriety is usually not a sensible goal for those hoping to avoid detection. This is what threat actors behind the prolific Emotet botnet learned earlier in 2021, for instance, when a coordinated effort was launched by eight national law enforcement agencies to take down their operation. There are, however, certain names which appear again and again in cyber security media and consistently avoid detection – names like Outlaw.

How Outlaw plans an ambush

Despite being active since 2018, very little is known about the hacking group Outlaw, which has staged numerous botnet and crypto-jacking attacks in China and internationally. The group is recognized by a variety of calling cards, be they repeated filenames or a tendency to illicitly mine Monero cryptocurrency, but its success ultimately lies in its tendency to adapt and evolve during months of dormancy between attacks.

Outlaw’s attacks are marked by constant changes and updates, which they work on in relative silence, before targeting security systems which are too-often defeated by the unfamiliarity of the threat.

In 2020, Outlaw gained attention when they updated their botnet toolset to find and eradicate other criminals’ crypto-jacking software, maximizing their own payout from infected devices. While it might come as no surprise that there’s no honor among cyber-thieves, this update also implemented more troubling changes which allowed Outlaw’s malware to evade traditional security defenses.

By switching disguises between each big robbery, and laying low with the loot, Outlaw ensures that traditional security systems which rely on historical attack data will never be ready for them, no matter how much notoriety is attached to their name. When organizations move beyond these systems’ rules-based approaches, however, adopting Self-Learning AI to protect their digital estates, they can begin to turn the tables on groups like Outlaw.

This blog explores how two pre-infected zombie devices in two very different parts of the world were activated by Outlaw’s botnet in the summer of 2021, and how Darktrace was able to detect the activity despite the devices being pre-infected.

Bounty hunting: First signs of attack

Figure 1: Timeline of the attack.

When a new device was added to the network of a Central American telecomms company in July, Darktrace detected a series of regular connections to two suspicious endpoints which it identified as beaconing behavior. The same behavior was noticed independently, but almost simultaneously, at a financial company in the APAC region, which was implementing Darktrace for the first time. Darktrace’s Self-Learning AI was able to identify the pre-infected devices by clustering similarly-behaving devices into peer groups within the local digital estates and therefore recognize that both were acting unusually based on a range of behaviors.

The first sign that the zombie devices had been activated by Outlaw was the initiation of cryptocurrency mining. Both devices, despite their geographical distance, were discovered to be connected to a single crypto-account, exemplifying the indiscriminate and exponential nature by which a botnet grows.

Outlaw has in the past restricted its activities to devices within China in what was assumed to be a show of caution, but recent activities like this one speak to a growing confidence.

The botnet recruitment process

The subsequent initiation of Internet Relay Chat (IRC) connections across port 443, a port more often associated with HTTPS activity, was perfectly characteristic of the Outlaw botnet’s earlier activity in 2020. IRC is a tool regularly used for communication between botmasters and zombie devices, but by using port 443 the attacker was attempting to blend into normal Internet traffic.

Soon after this exchange, the devices downloaded a shell script. Darktrace’s Cyber AI Analyst was able to intercept and recreate this shell script as it passed through the network, revealing its full function. Intriguingly, the script identified and excluded devices utilizing ARM architecture from the botnet. Due to its notably low battery consumption, ARM architecture is used primarily by portable mobile devices.

This selectivity is evidence that malicious crypto-mining remains Outlaw’s primary objective. By circumventing smaller devices which offer limited crypto-mining capabilities, this shell script focuses the botnet on the most high-powered, and therefore profitable, devices, such as desktop computers and servers. In this way, it reduces the Indicators of Compromise (IOCs) left behind by the wider botnet without greatly affecting the scale of its crypto-mining operation.

The two devices in question did not employ ARM architecture, and minutes later received a secondary payload containing a file named dota3[.]tar[.]gz, a sequel of sorts to the previous incarnation of the Outlaw botnet, ‘dota2’, which itself referenced a popular video game of the same name. With the arrival of this file, the devices appear to have been updated with the latest version of Outlaw’s world-spanning botnet.

This download was made possible in part by the attacker’s use of ‘Living off the Land’ tactics. By using only common Linux programs already present on the devices (‘curl’ and ‘Wget’ respectively), Outlaw had avoided having its activity flagged by traditional security systems. Wget, for instance, is ostensibly a reputable program used for retrieving content from web servers, and was never previously recorded as part of Outlaw’s TTPs (Tactics, Techniques, and Procedures).

By evolving and adapting its approach, Outlaw is continually able to outsmart and outrun rules-based security. Darktrace’s Self-Learning AI, however, kept pace, immediately identifying this Wget connection as suspicious and advising further investigation.

Figure 2: Cyber AI Analyst identifies Wget use on the morning of July 15 as suspicious and begins investigating potentially related HTTP connections made on the morning of July 14. In this way, it builds a complete picture of the attack.

The botnet unchained

In the following 36 hours, Darktrace detected over 6 million TCP and SSH connections directed to rare external IP addresses using ports often associated with SSH, such as 22, 2222, and 2022.

Exactly what the botnet was undertaking with these connections can only be speculated on. The devices may have been made part of a DDoS (Distributed Denial of Service) attack, bruteforce attempts on targeted SSH accounts, or simply have taken up the task of seeking and infecting new targets, further expanding the botnet. Darktrace recognized that neither device had made SSH connections prior to this event and, had Antigena been in active mode, would have enacted measures to stop them.

Figure 3: The behavior on the device before and after the bot was activated on July 14, 2021. The large spike in model breaches shows clear deviation from the established ‘pattern of life’.

Thankfully, the owners of both devices responded to Darktrace’s detection alerts soon enough to prevent any serious damage to their own digital estates. Had these devices remained under the influence of the botnet, the ramifications may have been far graver.

The use of SSH protocol would have allowed Outlaw to pivot into any number of activities, potentially compromising each device’s network further and causing data or monetary loss to their respective organizations.

Call the sheriff: Self-Learning AI

Rules-based security solutions operate much like the ‘wanted’ posters of the old west, looking out for the criminals who came through town last week without preparing for those riding over the hill today. When black hats and outlaws are adopting new looks and employing new techniques with every attack, a new way of responding to threats is needed.

Darktrace doesn’t need to know the name ‘Outlaw’, or the group’s history of evolving attacks, in order to stop them. With its fundamental self-learning approach, Darktrace learns its surroundings from the ground up, and identifies subtle deviations indicative of a cyber-threat. And with Autonomous Response, it will even take targeted action to neutralize the threat at machine speed, without the need for human intervention.

Thanks to Darktrace analyst Jun Qi Wong for his insights on the above threat find.

Learn more about how Cyber AI Analyst sheds light on complex attacks

Technical details

Darktrace model detections

  • Compliance / Crypto Currency Mining Activity
  • Compromise / High Priority Crypto Currency Mining [Enhanced Monitoring]
  • Anomalous Connection / New User Agent to IP Without Hostname
  • Anomalous File / Zip or Gzip from Rare External Location
  • Anomalous Connection / Application Protocol on Uncommon Port
  • Device / Increased External Connectivity
  • Unusual Activity / Unusual External Activity
  • Compromise / SSH Beacon
  • Compromise / High Frequency SSH Beacon
  • Anomalous Connection / Multiple Connections to New External TCP Port

Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Oakley Cox
Director of Product

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September 5, 2025

Cyber Assessment Framework v4.0 Raises the Bar: 6 Questions every security team should ask about their security posture

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What is the Cyber Assessment Framework?

The Cyber Assessment Framework (CAF) acts as guide for organizations, specifically across essential services, critical national infrastructure and regulated sectors, across the UK for assessing, managing and improving their cybersecurity, cyber resilience and cyber risk profile.

The guidance in the Cyber Assessment Framework aligns with regulations such as The Network and Information Systems Regulations (NIS), The Network and Information Security Directive (NIS2) and the Cyber Security and Resilience Bill.

What’s new with the Cyber Assessment Framework 4.0?

On 6 August 2025, the UK’s National Cyber Security Centre (NCSC) released Cyber Assessment Framework 4.0 (CAF v4.0) a pivotal update that reflects the increasingly complex threat landscape and the regulatory need for organisations to respond in smarter, more adaptive ways.

The Cyber Assessment Framework v4.0 introduces significant shifts in expectations, including, but not limited to:

  • Understanding threats in terms of the capabilities, methods and techniques of threat actors and the importance of maintaining a proactive security posture (A2.b)
  • The use of secure software development principles and practices (A4.b)
  • Ensuring threat intelligence is understood and utilised - with a focus on anomaly-based detection (C1.f)
  • Performance of proactive threat hunting with automation where appropriate (C2.a)

This blog post will focus on these components of the framework. However, we encourage readers to get the full scope of the framework by visiting the NCSC website where they can access the full framework here.

In summary, the changes to the framework send a clear signal: the UK’s technical authority now expects organisations to move beyond static rule-based systems and embrace more dynamic, automated defences. For those responsible for securing critical national infrastructure and essential services, these updates are not simply technical preferences, but operational mandates.

At Darktrace, this evolution comes as no surprise. In fact, it reflects the approach we've championed since our inception.

Why Darktrace? Leading the way since 2013

Darktrace was built on the principle that detecting cyber threats in real time requires more than signatures, thresholds, or retrospective analysis. Instead, we pioneered a self-learning approach powered by artificial intelligence, that understands the unique “normal” for every environment and uses this baseline to spot subtle deviations indicative of emerging threats.

From the beginning, Darktrace has understood that rules and lists will never keep pace with adversaries. That’s why we’ve spent over a decade developing AI that doesn't just alert, it learns, reasons, explains, and acts.

With Cyber Assessment Framework v4.0, the bar has been raised to meet this new reality. For technical practitioners tasked with evaluating their organisation’s readiness, there are five essential questions that should guide the selection or validation of anomaly detection capabilities.

6 Questions you should ask about your security posture to align with CAF v4

1. Can your tools detect threats by identifying anomalies?

Cyber Assessment Framework v4.0 principle C1.f has been added in this version and requires that, “Threats to the operation of network and information systems, and corresponding user and system behaviour, are sufficiently understood. These are used to detect cyber security incidents.”

This marks a significant shift from traditional signature-based approaches, which rely on known Indicators of Compromise (IOCs) or predefined rules to an expectation that normal user and system behaviour is understood to an extent enabling abnormality detection.

Why this shift?

An overemphasis on threat intelligence alone leaves defenders exposed to novel threats or new variations of existing threats. By including reference to “understanding user and system behaviour” the framework is broadening the methods of threat detection beyond the use of threat intelligence and historical attack data.

While CAF v4.0 places emphasis on understanding normal user and system behaviour and using that understanding to detect abnormalities and as a result, adverse activity. There is a further expectation that threats are understood in terms of industry specific issues and that monitoring is continually updated  

Darktrace uses an anomaly-based approach to threat detection which involves establishing a dynamic baseline of “normal” for your environment, then flagging deviations from that baseline — even when there’s no known IoCs to match against. This allows security teams to surface previously unseen tactics, techniques, and procedures in real time, whether it’s:

  • An unexpected outbound connection pattern (e.g., DNS tunnelling);
  • A first-time API call between critical services;
  • Unusual calls between services; or  
  • Sensitive data moving outside normal channels or timeframes.

The requirement that organisations must be equipped to monitor their environment, create an understanding of normal and detect anomalous behaviour aligns closely with Darktrace’s capabilities.

2. Is threat hunting structured, repeatable, and improving over time?

CAF v4.0 introduces a new focus on structured threat hunting to detect adverse activity that may evade standard security controls or when such controls are not deployable.  

Principle C2.a outlines the need for documented, repeatable threat hunting processes and stresses the importance of recording and reviewing hunts to improve future effectiveness. This inclusion acknowledges that reactive threat hunting is not sufficient. Instead, the framework calls for:

  • Pre-determined and documented methods to ensure threat hunts can be deployed at the requisite frequency;
  • Threat hunts to be converted  into automated detection and alerting, where appropriate;  
  • Maintenance of threat hunt  records and post-hunt analysis to drive improvements in the process and overall security posture;
  • Regular review of the threat hunting process to align with updated risks;
  • Leveraging automation for improvement, where appropriate;
  • Focus on threat tactics, techniques and procedures, rather than one-off indicators of compromise.

Traditionally, playbook creation has been a manual process — static, slow to amend, and limited by human foresight. Even automated SOAR playbooks tend to be stock templates that can’t cover the full spectrum of threats or reflect the specific context of your organisation.

CAF v4.0 sets the expectation that organisations should maintain documented, structured approaches to incident response. But Darktrace / Incident Readiness & Recovery goes further. Its AI-generated playbooks are bespoke to your environment and updated dynamically in real time as incidents unfold. This continuous refresh of “New Events” means responders always have the latest view of what’s happening, along with an updated understanding of the AI's interpretation based on real-time contextual awareness, and recommended next steps tailored to the current stage of the attack.

The result is far beyond checkbox compliance: a living, adaptive response capability that reduces investigation time, speeds containment, and ensures actions are always proportionate to the evolving threat.

3. Do you have a proactive security posture?

Cyber Assessment Framework v4.0 does not want organisations to detect threats, it expects them to anticipate and reduce cyber risk before an incident ever occurs. That is s why principle A2.b calls for a security posture that moves from reactive detection to predictive, preventative action.

A proactive security posture focuses on reducing the ease of the most likely attack paths in advance and reducing the number of opportunities an adversary has to succeed in an attack.

To meet this requirement, organisations could benefit in looking for solutions that can:

  • Continuously map the assets and users most critical to operations;
  • Identify vulnerabilities and misconfigurations in real time;
  • Model likely adversary behaviours and attack paths using frameworks like MITRE ATT&CK; and  
  • Prioritise remediation actions that will have the highest impact on reducing overall risk.

When done well, this approach creates a real-time picture of your security posture, one that reflects the dynamic nature and ongoing evolution of both your internal environment and the evolving external threat landscape. This enables security teams to focus their time in other areas such as  validating resilience through exercises such as red teaming or forecasting.

4. Can your team/tools customize detection rules and enable autonomous responses?

CAF v4.0 places greater emphasis on reducing false positives and acting decisively when genuine threats are detected.  

The framework highlights the need for customisable detection rules and, where appropriate, autonomous response actions that can contain threats before they escalate:

The following new requirements are included:  

  • C1.c.: Alerts and detection rules should be adjustable to reduce false positives and optimise responses. Custom tooling and rules are used in conjunction with off the shelf tooling and rules;
  • C1.d: You investigate and triage alerts from all security tools and take action – allowing for improvement and prioritization of activities;
  • C1.e: Monitoring and detection personnel have sufficient understanding of operational context and deal with workload effectively as well as identifying areas for improvement (alert or triage fatigue is not present);
  • C2.a: Threat hunts should be turned into automated detections and alerting where appropriate and automation should be leveraged to improve threat hunting.

Tailored detection rules improve accuracy, while automation accelerates response, both of which help satisfy regulatory expectations. Cyber AI Analyst allows for AI investigation of alerts and can dramatically reduce the time a security team spends on alerts, reducing alert fatigue, allowing more time for strategic initiatives and identifying improvements.

5. Is your software secure and supported?  

CAF v4.0 introduced a new principle which requires software suppliers to leverage an established secure software development framework. Software suppliers must be able to demonstrate:  

  • A thorough understanding of the composition and provenance of software provided;  
  • That the software development lifecycle is informed by a detailed and up to date understanding of threat; and  
  • They can attest to the authenticity and integrity of the software, including updates and patches.  

Darktrace is committed to secure software development and all Darktrace products and internally developed systems are developed with secure engineering principles and security by design methodologies in place. Darktrace commits to the inclusion of security requirements at all stages of the software development lifecycle. Darktrace is ISO 27001, ISO 27018 and ISO 42001 Certified – demonstrating an ongoing commitment to information security, data privacy and artificial intelligence management and compliance, throughout the organisation.  

6. Is your incident response plan built on a true understanding of your environment and does it adapt to changes over time?

CAF v4.0 raises the bar for incident response by making it clear that a plan is only as strong as the context behind it. Your response plan must be shaped by a detailed, up-to-date understanding of your organisation’s specific network, systems, and operational priorities.

The framework’s updates emphasise that:

  • Plans must explicitly cover the network and information systems that underpin your essential functions because every environment has different dependencies, choke points, and critical assets.
  • They must be readily accessible even when IT systems are disrupted ensuring critical steps and contact paths aren’t lost during an incident.
  • They should be reviewed regularly to keep pace with evolving risks, infrastructure changes, and lessons learned from testing.

From government expectation to strategic advantage

Cyber Assessment Framework v4.0 signals a powerful shift in cybersecurity best practice. The newest version sets a higher standard for detection performance, risk management, threat hunting software development and proactive security posture.

For Darktrace, this is validation of the approach we have taken since the beginning: to go beyond rules and signatures to deliver proactive cyber resilience in real-time.

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Disclaimer:

This document has been prepared on behalf of Darktrace Holdings Limited. It is provided for information purposes only to provide prospective readers with general information about the Cyber Assessment Framework (CAF) in a cyber security context. It does not constitute legal, regulatory, financial or any other kind of professional advice and it has not been prepared with the reader and/or its specific organisation’s requirements in mind. Darktrace offers no warranties, guarantees, undertakings or other assurances (whether express or implied)  that: (i) this document or its content are  accurate or complete; (ii) the steps outlined herein will guarantee compliance with CAF; (iii) any purchase of Darktrace’s products or services will guarantee compliance with CAF; (iv) the steps outlined herein are appropriate for all customers. Neither the reader nor any third party is entitled to rely on the contents of this document when making/taking any decisions or actions to achieve compliance with CAF. To the fullest extent permitted by applicable law or regulation, Darktrace has no liability for any actions or decisions taken or not taken by the reader to implement any suggestions contained herein, or for any third party products, links or materials referenced. Nothing in this document negates the responsibility of the reader to seek independent legal or other advice should it wish to rely on any of the statements, suggestions, or content set out herein.  

The cybersecurity landscape evolves rapidly, and blog content may become outdated or superseded. We reserve the right to update, modify, or remove any content without notice.

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September 5, 2025

Rethinking Signature-Based Detection for Power Utility Cybersecurity

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Lessons learned from OT cyber attacks

Over the past decade, some of the most disruptive attacks on power utilities have shown the limits of signature-based detection and reshaped how defenders think about OT security. Each incident reinforced that signatures are too narrow and reactive to serve as the foundation of defense.

2015: BlackEnergy 3 in Ukraine

According to CISA, on December 23, 2015, Ukrainian power companies experienced unscheduled power outages affecting a large number of customers — public reports indicate that the BlackEnergy malware was discovered on the companies’ computer networks.

2016: Industroyer/CrashOverride

CISA describes CrashOverride malwareas an “extensible platform” reported to have been used against critical infrastructure in Ukraine in 2016. It was capable of targeting industrial control systems using protocols such as IEC‑101, IEC‑104, and IEC‑61850, and fundamentally abused legitimate control system functionality to deliver destructive effects. CISA emphasizes that “traditional methods of detection may not be sufficient to detect infections prior to the malware execution” and recommends behavioral analysis techniques to identify precursor activity to CrashOverride.

2017: TRITON Malware

The U.S. Department of the Treasury reports that the Triton malware, also known as TRISIS or HatMan, was “designed specifically to target and manipulate industrial safety systems” in a petrochemical facility in the Middle East. The malware was engineered to control Safety Instrumented System (SIS) controllers responsible for emergency shutdown procedures. During the attack, several SIS controllers entered a failed‑safe state, which prevented the malware from fully executing.

The broader lessons

These events revealed three enduring truths:

  • Signatures have diminishing returns: BlackEnergy showed that while signatures can eventually identify adapted IT malware, they arrive too late to prevent OT disruption.
  • Behavioral monitoring is essential: CrashOverride demonstrated that adversaries abuse legitimate industrial protocols, making behavioral and anomaly detection more effective than traditional signature methods.
  • Critical safety systems are now targets: TRITON revealed that attackers are willing to compromise safety instrumented systems, elevating risks from operational disruption to potential physical harm.

The natural progression for utilities is clear. Static, file-based defenses are too fragile for the realities of OT.  

These incidents showed that behavioral analytics and anomaly detection are far more effective at identifying suspicious activity across industrial systems, regardless of whether the malicious code has ever been seen before.

Strategic risks of overreliance on signatures

  • False sense of security: Believing signatures will block advanced threats can delay investment in more effective detection methods.
  • Resource drain: Constantly updating, tuning, and maintaining signature libraries consumes valuable staff resources without proportional benefit.
  • Adversary advantage: Nation-state and advanced actors understand the reactive nature of signature defenses and design attacks to circumvent them from the start.

Recommended Alternatives (with real-world OT examples)

 Alternative strategies for detecting cyber attacks in OT
Figure 1: Alternative strategies for detecting cyber attacks in OT

Behavioral and anomaly detection

Rather than relying on signatures, focusing on behavior enables detection of threats that have never been seen before—even trusted-looking devices.

Real-world insight:

In one OT setting, a vendor inadvertently left a Raspberry Pi on a customer’s ICS network. After deployment, Darktrace’s system flagged elastic anomalies in its HTTPS and DNS communication despite the absence of any known indicators of compromise. The alerting included sustained SSL increases, agent‑beacon activity, and DNS connections to unusual endpoints, revealing a possible supply‑chain or insider risk invisible to static tools.  

Darktrace’s AI-driven threat detection aligns with the zero-trust principle of assuming the risk of a breach. By leveraging AI that learns an organization’s specific patterns of life, Darktrace provides a tailored security approach ideal for organizations with complex supply chains.

Threat intelligence sharing & building toward zero-trust philosophy

Frameworks such as MITRE ATT&CK for ICS provide a common language to map activity against known adversary tactics, helping teams prioritize detections and response strategies. Similarly, information-sharing communities like E-ISAC and regional ISACs give utilities visibility into the latest tactics, techniques, and procedures (TTPs) observed across the sector. This level of intel can help shift the focus away from chasing individual signatures and toward building resilience against how adversaries actually operate.

Real-world insight:

Darktrace’s AI embodies zero‑trust by assuming breach potential and continually evaluating all device behavior, even those deemed trusted. This approach allowed the detection of an anomalous SharePoint phishing attempt coming from a trusted supplier, intercepted by spotting subtle patterns rather than predefined rules. If a cloud account is compromised, unauthorized access to sensitive information could lead to extortion and lateral movement into mission-critical systems for more damaging attacks on critical-national infrastructure.

This reinforces the need to monitor behavioral deviations across the supply chain, not just known bad artifacts.

Defense-in-Depth with OT context & unified visibility

OT environments demand visibility that spans IT, OT, and IoT layers, supported by risk-based prioritization.

Real-world insight:

Darktrace / OT offers unified AI‑led investigations that break down silos between IT and OT. Smaller teams can see unusual outbound traffic or beaconing from unknown OT devices, swiftly investigate across domains, and get clear visibility into device behavior, even when they lack specialized OT security expertise.  

Moreover, by integrating contextual risk scoring, considering real-world exploitability, device criticality, firewall misconfiguration, and legacy hardware exposure, utilities can focus on the vulnerabilities that genuinely threaten uptime and safety, rather than being overwhelmed by CVE noise.  

Regulatory alignment and positive direction

Industry regulations are beginning to reflect this evolution in strategy. NERC CIP-015 requires internal network monitoring that detects anomalies, and the standard references anomalies 15 times. In contrast, signature-based detection is not mentioned once.

This regulatory direction shows that compliance bodies understand the limitations of static defenses and are encouraging utilities to invest in anomaly-based monitoring and analytics. Utilities that adopt these approaches will not only be strengthening their resilience but also positioning themselves for regulatory compliance and operational success.

Conclusion

Signature-based detection retains utility for common IT malware, but it cannot serve as the backbone of security for power utilities. History has shown that major OT attacks are rarely stopped by signatures, since each campaign targets specific systems with customized tools. The most dangerous adversaries, from insiders to nation-states, actively design their operations to avoid detection by signature-based tools.

A more effective strategy prioritizes behavioral analytics, anomaly detection, and community-driven intelligence sharing. These approaches not only catch known threats, but also uncover the subtle anomalies and novel attack techniques that characterize tomorrow’s incidents.

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About the author
Daniel Simonds
Director of Operational Technology
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