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September 11, 2023

Darktrace & FERC Order 887: Enhancing Cybersecurity

Understand Darktrace's role in supporting FERC Order 887 and its efforts to improve cybersecurity measures.
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
Jeffrey Macre
Industrial Security Solutions Architect
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11
Sep 2023

At a glance:

  • Darktrace/OT leverages machine learning to provide actionable preventative analytics, relevant real time anomaly based threat detection, and a variety of response capabilities as a full suite protection for OT/ICS operations Purdue levels 5-0.
  • Self-Learning AI detects and responds to cyber threats including malicious or non malicious insiders and supply chain attacks.
  • Darktrace/OT deploys passively within NERC CIP environments providing visibility without the need for any external connectivity or threat intelligence updates.

What is FERC?

The US Federal Energy Regulatory Commission (FERC) is responsible for the regulation of the wholesale electricity and natural gas transmission. FERC sits above the North American Electric Reliability Corporation (NERC) which is responsible for the development and enforcement of reliability standards for the US bulk power system. NERC CIP reliability standards are standards enforced by NERC to ensure the safety and protection of the bulk electric system.

What is FERC order 887?

In review of the CIP requirements, FERC identified a security gap. The gap was that there is no requirement for internal network security monitoring (INSM) within the security perimeters of CIP networked systems. Without this requirement and protections in place, if an attacker was to breach the security perimeter of the CIP networked environment, the victim organization would have no capability of detecting and alerting to what the adversary is doing within the security perimeter.  

FERC Order 887 is a final rule issued intended to direct NERC to develop new or modified reliability standards requiring internal network security monitoring INSM within Critical Infrastructure Protection (CIP) networked environments. A focus is placed on anomaly based detection used within the security perimeter so that threats without known rules and signatures associated, including insider threat and supply chain attacks, can be detected based on anomalous network activity within the CIP networked environment.

FERC order 887 specifically focuses on the need for addressing the INSM gap for BES high impact power generation systems with CIP networked environments with and without external connectivity and medium impact systems with external connectivity.

FERC Order 887 Requirements

1. Any new or modified CIP Reliability Standards should address the need for responsible entities to develop baselines of their network traffic inside their CIP-networked environment for BES Medium impact with external routable network connectivity and high impact with or without external routable network connectivity.

2. Any new or modified CIP Reliability Standards should address the need for responsible entities to monitor for and detect unauthorized activity, connections, devices, and software inside the CIP-networked environment. This should be done so that sophisticated threats including those that may already have persistent access to CIP networked systems, insider threats and supply chain threats can be detected at earlier stages.

3. Any new or modified CIP Reliability Standards should require responsible entities to identify anomalous activity to a high level of confidence by:  (1) logging network traffic (we note that packet capture is one means of accomplishing this goal); (2) maintaining logs and other data collected regarding network traffic.

How does Darktrace support FERC order 887?

For security professionals to satisfy FERC order 887, it is ideal to deploy an INSM that leverages anomaly based detection and is capable of detecting insider threats and supply chain attacks within CIP networked environments in medium and high impact power generation sites. Additionally, the INSM has to be able to function within high impact sites without any external network connectivity.

Darktrace/OT leverages machine learning to provide actionable preventative analytics, relevant real time anomaly based threat detection, and a variety of response capabilities as a full suite protection for OT/ICS operations Purdue levels 5-0, helping security professionals accommodate for FERC order 887 requirements.

Anomaly Based Detection

Darktrace establishes baseline and normal network activity via passive traffic analysis when monitoring the CIP-networked OT system. The baseline or “pattern of life” is then used to detect anomalies within the environment including unauthorized activity, connections, devices, and software inside the CIP-networked environment via anomaly-based detection.  

Darktrace’s AI technology uses unsupervised machine learning to identify anomalous activity to a high statistical level of confidence by logging network traffic via packet capture and maintaining logs and other data collected regarding network traffic inherently within the platform for 1 year.

All log data stored by Darktrace can be exported to other systems so that it can be stored longer than 1 year. If you need to retain logs for more than 1 year, Darktrace can offload the logs to retain indefinitely.

Figure 1: AI Analyst Incident reporting an unusual reprogram command using the MODBUS protocol. The incident includes a plain English summary, relevant technical information, and the investigation process used by the AI.

Self-Learning AI

Darktrace/OT analyzes network traffic passively and learns the normal pattern of life of the these assets and their details (make, model, firmware, protocols, etc.). Darktrace/OT does not need any data or threat feeds from external sources because the AI builds an innate understanding of self without third-party support.

Darktrace is capable of detecting sophisticated novel malware-based attacks as well as supply chain attacks, insider threats, and other attacks where the adversary has established foothold or persistent legitimized access to systems and cannot be detected by rules and signatures-based detection systems.

Darktrace/OT is an intelligent decision-making engine that uses its evolving understanding of your industrial organization to prompt targeted, non-disruptive action to contain emerging attacks, actively responding to security events occurring within the security perimeter autonomously or via human confirmation using TCP/resets or Darktrace can respond at security boundaries via various integrations with network security tools including firewalls and OT zero trust solutions.

Figure 2: The Darktrace Threat Visualizer allows security analysts and OT engineers to visualize and replay incidents in real time.

Deploys in Isolation Without External Connectivity

Darktrace/OT can deploy passively without the need for any external network connectivity into any low, medium, or high impact power generation facilities and maintain 100 percent integrity of the existing segmentation including fully air gapped environments.

Once Darktrace/OT is deployed, Darktrace immediately begins monitoring, learning, and analyzing the raw OT network traffic (east/west and north/south) within the CIP-networked environment creating a live data flow topology and baseline of network connectivity.

Because all data-processing and analytics are performed locally on the Darktrace appliance, there is no requirement for Darktrace to have a connection out to the internet. As a result, Darktrace/OT provides visibility and threat detection to air-gapped or highly segmented networks without jeopardizing their integrity. If a human or machine displays even the most nuanced forms of threatening behavior, the solution can illuminate this in real time.

Attack Case Study: Insider Threat

In the real-world example below, Darktrace/OT detected a subtle deviation from normal behavior when a reprogram command was sent by an engineering workstation to a PLC controlling a pump, an action an insider threat with legitimized access to OT systems would take to alter the physical process without any malware involved. In this instance, AI Analyst, Darktrace’s investigation tool that triages events to reveal the full security incident, detected the event as unusual based on multiple metrics including the source of the command, the destination device, the time of the activity, and the command itself.  

As a result, AI Analyst created a complete security incident, with a natural language summary, the technical details of the activity, and an investigation process explaining how it came to its conclusion. By leveraging Explainable AI, a security team can quickly triage and escalate Darktrace incidents in real time before it becomes disruptive, and even when performed by a trusted insider.

Figure 3: AI Analyst Incident reporting an unusual reprogram command using the MODBUS protocol. The incident includes a plain English summary, relevant technical information, and the investigation process used by the AI.

Credit to Daniel Simonds and Oakley Cox for their contribution to this blog.

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
Jeffrey Macre
Industrial Security Solutions Architect

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May 8, 2025

Anomaly-based threat hunting: Darktrace's approach in action

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What is threat hunting?

Threat hunting in cybersecurity involves proactively and iteratively searching through networks and datasets to detect threats that evade existing automated security solutions. It is an important component of a strong cybersecurity posture.

There are several frameworks that Darktrace analysts use to guide how threat hunting is carried out, some of which are:

  • MITRE Attack
  • Tactics, Techniques, Procedures (TTPs)
  • Diamond Model for Intrusion Analysis
  • Adversary, Infrastructure, Victims, Capabilities
  • Threat Hunt Model – Six Steps
  • Purpose, Scope, Equip, Plan, Execute, Feedback
  • Pyramid of Pain

These frameworks are important in baselining how to run a threat hunt. There are also a combination of different methods that allow defenders diversity– regardless of whether it is a proactive or reactive threat hunt. Some of these are:

  • Hypothesis-based threat hunting
  • Analytics-driven threat hunting
  • Automated/machine learning hunting
  • Indicator of Compromise (IoC) hunting
  • Victim-based threat hunting

Threat hunting with Darktrace

At its core, Darktrace relies on anomaly-based detection methods. It combines various machine learning types that allows it to characterize what constitutes ‘normal’, based on the analysis of many different measures of a device or actor’s behavior. Those types of learning are then curated into what are called models.

Darktrace models leverage anomaly detection and integrate outputs from Darktrace Deep Packet Inspection, telemetry inputs, and additional modules, creating tailored activity detection.

This dynamic understanding allows Darktrace to identify, with a high degree of precision, events or behaviors that are both anomalous and unlikely to be benign.  On top of machine learning models for detection, there is also the ability to change and create models showcasing the tool’s diversity. The Model Editor allows security teams to specify values, priorities, thresholds, and actions they want to detect. That means a team can create custom detection models based on specific use cases or business requirements. Teams can also increase the priority of existing detections based on their own risk assessments to their environment.

This level of dexterity is particularly useful when conducting a threat hunt. As described above, and in previous ‘Inside the SOC’ blogs such a threat hunt can be on a specific threat actor, specific sector, or a  hypothesis-based threat hunt combined with ‘experimenting’ with some of Darktrace’s models.

Conducting a threat hunt in the energy sector with experimental models

In Darktrace’s recent Threat Research report “AI & Cybersecurity: The state of cyber in UK and US energy sectors” Darktrace’s Threat Research team crafted hypothesis-driven threat hunts, building experimental models and investigating existing models to test them and detect malicious activity across Darktrace customers in the energy sector.

For one of the hunts, which hypothesised utilization of PerfectData software and multi-factor authentication (MFA) bypass to compromise user accounts and destruct data, an experimental model was created to detect a Software-as-a-Service (SaaS) user performing activity relating to 'PerfectData Software’, known to allow a threat actor to exfiltrate whole mailboxes as a PST file. Experimental model alerts caused by this anomalous activity were analyzed, in conjunction with existing SaaS and email-related models that would indicate a multi-stage attack in line with the hypothesis.

Whilst hunting, Darktrace researchers found multiple model alerts for this experimental model associated with PerfectData software usage, within energy sector customers, including an oil and gas investment company, as well as other sectors. Upon further investigation, it was also found that in June 2024, a malicious actor had targeted a renewable energy infrastructure provider via a PerfectData Software attack and demonstrated intent to conduct an Operational Technology (OT) attack.

The actor logged into Azure AD from a rare US IP address. They then granted Consent to ‘eM Client’ from the same IP. Shortly after, the actor granted ‘AddServicePrincipal’ via Azure to PerfectData Software. Two days later, the actor created a  new email rule from a London IP to move emails to an RSS Feed Folder, stop processing rules, and mark emails as read. They then accessed mail items in the “\Sent” folder from a malicious IP belonging to anonymization network,  Private Internet Access Virtual Private Network (PIA VPN) [1]. The actor then conducted mass email deletions, deleting multiple instances of emails with subject “[Name] shared "[Company Name] Proposal" With You” from the  “\Sent folder”. The emails’ subject suggests the email likely contains a link to file storage for phishing purposes. The mass deletion likely represented an attempt to obfuscate a potential outbound phishing email campaign.

The Darktrace Model Alert that triggered for the mass deletes of the likely phishing email containing a file storage link.
Figure 1: The Darktrace Model Alert that triggered for the mass deletes of the likely phishing email containing a file storage link.

A month later, the same user was observed downloading mass mLog CSV files related to proprietary and Operational Technology information. In September, three months after the initial attack, another mass download of operational files occurred by this actor, pertaining to operating instructions and measurements, The observed patience and specific file downloads seemingly demonstrated an intent to conduct or research possible OT attack vectors. An attack on OT could have significant impacts including operational downtime, reputational damage, and harm to everyday operations. Darktrace alerted the impacted customer once findings were verified, and subsequent actions were taken by the internal security team to prevent further malicious activity.

Conclusion

Harnessing the power of different tools in a security stack is a key element to cyber defense. The above hypothesis-based threat hunt and custom demonstrated intent to conduct an experimental model creation demonstrates different threat hunting approaches, how Darktrace’s approach can be operationalized, and that proactive threat hunting can be a valuable complement to traditional security controls and is essential for organizations facing increasingly complex threat landscapes.

Credit to Nathaniel Jones (VP, Security & AI Strategy, Field CISO at Darktrace) and Zoe Tilsiter (EMEA Consultancy Lead)

References

  1. https://spur.us/context/191.96.106.219

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About the author
Nathaniel Jones
VP, Security & AI Strategy, Field CISO

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May 6, 2025

Combatting the Top Three Sources of Risk in the Cloud

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With cloud computing, organizations are storing data like intellectual property, trade secrets, Personally Identifiable Information (PII), proprietary code and statistics, and other sensitive information in the cloud. If this data were to be accessed by malicious actors, it could incur financial loss, reputational damage, legal liabilities, and business disruption.

Last year data breaches in solely public cloud deployments were the most expensive type of data breach, with an average of $5.17 million USD, a 13.1% increase from the year before.

So, as cloud usage continues to grow, the teams in charge of protecting these deployments must understand the associated cybersecurity risks.

What are cloud risks?

Cloud threats come in many forms, with one of the key types consisting of cloud risks. These arise from challenges in implementing and maintaining cloud infrastructure, which can expose the organization to potential damage, loss, and attacks.

There are three major types of cloud risks:

1. Misconfigurations

As organizations struggle with complex cloud environments, misconfiguration is one of the leading causes of cloud security incidents. These risks occur when cloud settings leave gaps between cloud security solutions and expose data and services to unauthorized access. If discovered by a threat actor, a misconfiguration can be exploited to allow infiltration, lateral movement, escalation, and damage.

With the scale and dynamism of cloud infrastructure and the complexity of hybrid and multi-cloud deployments, security teams face a major challenge in exerting the required visibility and control to identify misconfigurations before they are exploited.

Common causes of misconfiguration come from skill shortages, outdated practices, and manual workflows. For example, potential misconfigurations can occur around firewall zones, isolated file systems, and mount systems, which all require specialized skill to set up and diligent monitoring to maintain

2. Identity and Access Management (IAM) failures

IAM has only increased in importance with the rise of cloud computing and remote working. It allows security teams to control which users can and cannot access sensitive data, applications, and other resources.

Cybersecurity professionals ranked IAM skills as the second most important security skill to have, just behind general cloud and application security.

There are four parts to IAM: authentication, authorization, administration, and auditing and reporting. Within these, there are a lot of subcomponents as well, including but not limited to Single Sign-On (SSO), Two-Factor Authentication (2FA), Multi-Factor Authentication (MFA), and Role-Based Access Control (RBAC).

Security teams are faced with the challenge of allowing enough access for employees, contractors, vendors, and partners to complete their jobs while restricting enough to maintain security. They may struggle to track what users are doing across the cloud, apps, and on-premises servers.

When IAM is misconfigured, it increases the attack surface and can leave accounts with access to resources they do not need to perform their intended roles. This type of risk creates the possibility for threat actors or compromised accounts to gain access to sensitive company data and escalate privileges in cloud environments. It can also allow malicious insiders and users who accidentally violate data protection regulations to cause greater damage.

3. Cross-domain threats

The complexity of hybrid and cloud environments can be exploited by attacks that cross multiple domains, such as traditional network environments, identity systems, SaaS platforms, and cloud environments. These attacks are difficult to detect and mitigate, especially when a security posture is siloed or fragmented.  

Some attack types inherently involve multiple domains, like lateral movement and supply chain attacks, which target both on-premises and cloud networks.  

Challenges in securing against cross-domain threats often come from a lack of unified visibility. If a security team does not have unified visibility across the organization’s domains, gaps between various infrastructures and the teams that manage them can leave organizations vulnerable.

Adopting AI cybersecurity tools to reduce cloud risk

For security teams to defend against misconfigurations, IAM failures, and insecure APIs, they require a combination of enhanced visibility into cloud assets and architectures, better automation, and more advanced analytics. These capabilities can be achieved with AI-powered cybersecurity tools.

Such tools use AI and automation to help teams maintain a clear view of all their assets and activities and consistently enforce security policies.

Darktrace / CLOUD is a Cloud Detection and Response (CDR) solution that makes cloud security accessible to all security teams and SOCs by using AI to identify and correct misconfigurations and other cloud risks in public, hybrid, and multi-cloud environments.

It provides real-time, dynamic architectural modeling, which gives SecOps and DevOps teams a unified view of cloud infrastructures to enhance collaboration and reveal possible misconfigurations and other cloud risks. It continuously evaluates architecture changes and monitors real-time activity, providing audit-ready traceability and proactive risk management.

Real-time visibility into cloud assets and architectures built from network, configuration, and identity and access roles. In this unified view, Darktrace / CLOUD reveals possible misconfigurations and risk paths.
Figure 1: Real-time visibility into cloud assets and architectures built from network, configuration, and identity and access roles. In this unified view, Darktrace / CLOUD reveals possible misconfigurations and risk paths.

Darktrace / CLOUD also offers attack path modeling for the cloud. It can identify exposed assets and highlight internal attack paths to get a dynamic view of the riskiest paths across cloud environments, network environments, and between – enabling security teams to prioritize based on unique business risk and address gaps to prevent future attacks.  

Darktrace’s Self-Learning AI ensures continuous cloud resilience, helping teams move from reactive to proactive defense.

[related-resource]

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About the author
Pallavi Singh
Product Marketing Manager, OT Security & Compliance
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