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
Dr. Oakley Cox-Robinson
Senior Director of Product
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03
Aug 2023
What is an Insider Threat?
Insider threat refers to cyber threats which originates from within an organization. Insider threats can come in the form of employees, vendors, contractors, or anyone with access to sensitive systems, data, or information. The cyber risk posed by insiders can be grouped into malicious insiders, such as rogue or disgruntled employees, or accidental, such as a well-meaning employee inadvertently leaking data or introducing a security flaw.
Insider threats are a reality for all organizations, across all industry verticals around the world. However, this Darktrace short read will focus on Operational Technology (OT), such as those within Critical Infrastructure, and the impact insiders can have on safety-critical systems, the environment, and human life.
Malicious v Non-Malicious Insider Threat in OT
There are generally two types of insider threats: malicious and non-malicious, or accidental. For organizations managing OT, both types originate from personnel who have legitimate privileged access to OT networks and have insider knowledge of assets, configurations, locations, security controls, or vulnerabilities. Of increasing concern to security teams, these personnel can also include external contractors, such as vendors or consultants, who require high levels of access to perform their role.
The 2001 sewage spill in Maroochy Shire, Australia, was the first high-profile example of a malicious insider manipulating control systems to impact OT. More recently, the 2021 incident at the Oldsmar Water Facility in Florida was the result of poor cyber security practices. While there is much speculation as to the exact cause of the incident, the root cause appears to have been human error which resulted in changes to intended chemical content levels in drinking water.
Insider Threats: Challenges and Solutions
The biggest concern for cyber security managers in Critical Infrastructure and OT networks is the threat posed by those on the inside. Compliance breaches, poor cyber hygiene, and disgruntled or rogue employees all pose a greater everyday threat to these systems than APTs or the latest zero day.
Insider threats are hard to catch. They rarely use attack tools or malware to achieve their goal, rendering signature-based threat detection useless. Instead, they leverage their legitimate access to make changes to native functionality. Rules-based threat detection can be used to prevent certain actions, but playbooks are limited to the imagination of the person implementing them and the time they have to create and maintain them.
Anomaly-Based Threat Detection
Anomaly-based threat detection is uniquely positioned to detect insider threats. Both accidental and malicious disruption may use legitimate privileged access to target Purdue Level 1 and 2 controllers and programmers to alter operations. The actor will alter the routine functionality of the process control environment, which can be detected and alerted by a security tool which understands normal and can spot deviations.
Darktrace/OT vs Insider Threats
Powered by scalable, Self-Learning AI, Darktrace/OT uses anomaly-based detection to detect unpredictable attacks in their earliest stages. By learning the normal ‘patterns of life’ for every device and operator in an industrial environment, Darktrace/OT detects known and unknown threats including zero-day exploits, supply chain attacks, ransomware, pre-existing infections, and insider threat.
Using raw digital data from an OT network to understand the normal pattern of life, Darktrace/OT does not need any data or threat feeds from external sources to perform anomaly-based threat detection. The approach is perfectly suited to spotting and stopping the threat posed by malicious and non-malicious insiders.
Attack Case Study: Spotting Insider Threats with Self-Learning AI
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 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.
Figure 2: The Darktrace Threat Visualizer allows security analysts and OT engineers to visualize and replay incidents in real time.
Credit to Daniel Simonds for his contribution to this blog.
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.
Beyond the Fridge: Exposed Industrial Control Systems
Following the cyber attack on supermarket refrigeration systems in France, Darktrace researchers looked at how many industrial devices are sitting exposed on the public internet. From fridge controllers to PLCs and remote gateways, many rely on weak or default credentials.
Botnet Behind the Camera: Mirai Katana Activity on a Video Recording Device
Darktrace identified Mirai Katana botnet activity on a camera device in a sports-sector network, showing how compromised IoT devices can facilitate malware delivery, botnet activity, and data exfiltration from inside trusted environments.
A stadium during a major sporting event is simultaneously a city, a broadcast hub, a transport node, a public safety space, and a national symbol. That makes them attractive not just to criminals, but to politically motivated and state‑aligned threat actors willing to plan years ahead. With the 2026 FIFA World Cup spanning three nations and dozens of host cities, those challenges – and the AI now amplifying them – have never carried higher stakes.
“I wonder if running that tool influenced the connectivity in some way.”
“I might need to target down or reboot, but first, I'll investigate the network using ARP. I wonder if ARP is responding properly. If I see "ip neigh stale," that could indicate a problem. I think arping might help in this situation, but I’ll need to keep an eye on how the network is behaving. It's all about figuring out where the issue lies!”
図10：エージェントの異常なLDAPアクティビティに対してトリガーされたこのLinuxサーバーに対するモデル検知は、Cyber AI Analystによって自律的に調査され、その調査結果にはエージェントの異常なSMBセッションアクティビティに関する内容が含まれ、Cyber AIAnalystインシデントに記録されました。
DarktraceのCyber AI Analystは、エージェントのアクティビティによってトリガーされたモデル検知を自律的に調査し、その調査結果を1つの重要なインシデントレポートにまとめました。インシデント報告書の内容は、エージェントによるネットワークスキャンから始まりコーディング演習内容のSMBベースの上書きで終わる、エージェントの行動の各段階を詳しくカバーしています。
図20：Cyber AI Analystは、モデル検知に対する自律的な調査を通じて得られた所見をつなぎ合わせ、エージェントの進行状況のタイムラインを作成し、アクティビティを視覚的に表したグラフとともに提供しました。
Cyber Verification Program下のAnthropic Opus 4.6モデルなど、他のフロンティアモデルを使用したエージェントも、Darktraceのシミュレートされた環境で同じ設定とタスクを使ってテストされました。反復の度に、エージェントは演習の内容または採点プログラムを変更して不正を行おうとしました。基盤となるモデルに関係なく、エージェントは自分が置かれた環境を侵害することで、100％のスコアを達成する方法を見つけることに成功していました。これらのエージェントによるハッキングは、アクティビティの内容こそ違っていましたが、すべてその環境のベースラインの振る舞いから大きく逸脱した動作を引き起こしていました。これらの逸脱はDarktraceプラットフォームに十分な検知の機会を提供し、エージェントの逃げ場はありませんでした。
エージェンティックハーネスは会話履歴をローカルに保存しますが、ダークトレースの研究者たちは、保存されたAIの応答が本当にモデルによって生成されたものであるかどうかの検証が行われていないことを発見しました。また、この設計上の選択がAnthropic Claude Code、AWS Kiro-CLI、OpenAI Codex、およびオープンソースのPiにわたって共通していることを確認しました。
会話履歴は多くの場合クライアント側に保存されます。例えば、Anthropic Claude Code、OpenAI Codex、AWS Kiro-CLI、Piなどでもそうです。そのため、ユーザーはセッションを自由に再開でき、一部の製品にはその履歴を操作する機能が組み込まれています。たとえば、会話の中の特定の時点に戻り、送信したメッセージを編集して、会話を別の軌道へ進めていくことができます。きわめて重要な点として、私たちが調査したすべてのケースにおいて、保存されているAIの応答が対応するモデルから生成されたものであり改ざんされていないことの検証は行われていませんでした。
