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
Wayne Racey
Manager of IT Operations, City of St Catharines (Guest Contributor)
Share
08
Aug 2022
The City of St. Catharines is the largest city in Ontario, Canada’s Niagara Region. We strive to meet the needs of our over 140,000 residents. Cyber disruption could stop our municipality from functioning, so having a strong security stack is critical to our mission.
Globally, 44% of ransomware attacks target municipalities. In Canada, smaller cities have had to deal with increased attempts by threat actors to access information, without significant increases in security staff or budgets.
Data breaches incur an average cost totaling $6.35 million CAD because of ransomware payments, fines for leaked personally identifiable information, or recovery costs. That number does not quantify the additional reputational damage, PR setbacks, and other repercussions. Instead of resigning ourselves to accepting a greater cyber-risk, we turned to Darktrace to protect our network, email, and Microsoft 365 Suite.
How Self-Learning AI buys back time
I’m sure we as a municipality are grappling with the same issues that other cities of a similar size face from a budgetary standpoint. We do not have enough boots on the ground and our IT team is stretched thin. Investigating cyber security incidents takes a lot of time. We must find correlations between several old systems and manually go through security event logs to determine which incidents require follow-up. These factors greatly increased our response time.
When we first implemented Darktrace, we immediately saw that it does all the heavy lifting for us when it comes to the analysis of breach events. The Cyber AI Analyst shows a granular breakdown of the digital traffic coming into and out of the City, all on a single screen. This helps us separate the meaningful data from the noise.
I now start all my investigations with the Cyber AI Analyst. It sets me up with actionable insights that ensure I focus my time and energy in the most productive ways.
Darktrace also saves my team time and labor when it comes to responding to incidents. When it does detect attacks, it autonomously responds in seconds to contain them without interfering with any normal business operations.
We have been able to configure Darktrace’s settings to further streamline our workload. We’ve made several adjustments that reduce the number of helpdesk tickets my team receives, which ensures we’re spending our time on high-value work.
Darktrace not only makes up for the limited resources of our IT team, but also augments us. By simplifying our investigations and autonomously stopping attacks, Darktrace gives us more time to work on our other IT responsibilities without worrying about our security.
Darktrace/Network brings visibility and defense
Before Darktrace, we didn’t have visibility into the east-west traffic on our network. Once installed, it provided a view of traffic we had never anticipated, and we saw connections that we never even knew existed.
Darktrace/Network has insight into every laptop, server, phone, and user. The Self-Learning AI learns the “pattern of life” of our organization, so that it can recognize unusual activity that indicates a cyber-attack. In the case of a serious emerging attack, Darktrace RESPOND can take precise actions to stop it while otherwise allowing normal digital operations.
Darktrace/Network maps connections made within our network, whether between users and servers or between devices. It sorts users into groups that behave similarly, making it more obvious if one acts unusually. Darktrace/Email and Darktrace/Apps extend this coverage to our email and Microsoft 365 Suite, respectively. In this way, Darktrace allows us to see comprehensively into end-user traffic.
Darktrace can stop attempts to download malicious software, move malware laterally, upload private data, and everything in between. This means we are protected from attacks that are notoriously difficult to find, such as stealth attacks, machine speed ransomwares, insider threats, and zero-days.
Darktrace brings peace of mind
The Self-Learning AI has transformed my skepticism of AI into enthusiasm. I now see the possibilities with AI are limited only by one’s imagination, and the Darktrace team has harnessed it to create a great security tool.
Darktrace has proven to be the addition we needed to keep our digital landscape secure while contending with the limitations of budget and staffing during a time of increasingly frequent attacks targeting municipalities. My team’s support for Darktrace has been outstanding, and we have no regrets.
Darktrace gives us the assurance that no matter what rules we put in place regarding the flow of traffic on our network, it will always be present to reconfigure our defenses and safeguard our digital assets should an attack occur. It works 24/7, at machine speed, and augments our IT team. That defines peace of mind!
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
Wayne Racey
Manager of IT Operations, City of St Catharines (Guest Contributor)
Why Kubernetes Security Needs A Behavioral Approach
As Kubernetes adoption accelerates, especially for AI workloads, Darktrace extends behavioral security across runtime activity, delivering visibility, detection, response, forensics, and risk context to identify and contain threats.
Darktrace researchers investigate CikaBika Bot, a Rust-based remote access trojan delivered through a document-themed lure. The malware uses a Nim loader, PowerShell UAC bypass and DLL search-order hijacking before leveraging Discord for C2, reconnaissance, remote execution, and data exfiltration through ngrok in observed malicious activity.
A Chain Reaction: Blockchain-Hosted Infostealer Campaign Targets Windows and macOS
Darktrace detected a blockchain-hosted infostealer campaign targeting Windows and macOS devices, combining ClickFix social engineering with the abuse of legitimate services such as GitHub, blockchain infrastructure, and DGA C2 endpoints to evade disruption and support malware delivery.
“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。
.jpeg)
