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May 20, 2025

Adapting to new USCG cybersecurity mandates: Darktrace for ports and maritime systems

Darktrace uses AI-led OT, IoT, and IT Network Security to help secure maritime transportation systems. This blog describes some of the new mandated requirements by the USCG and demonstrates Darktrace’s security capabilities.
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
Daniel Simonds
Director of Operational Technology
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20
May 2025

What is the Marine Transportation System (MTS)?

Marine Transportation Systems (MTS) play a substantial roll in U.S. commerce, military readiness, and economic security. Defined as a critical national infrastructure, the MTS encompasses all aspects of maritime transportation from ships and ports to the inland waterways and the rail and roadways that connect them.

MTS interconnected systems include:

  • Waterways: Coastal and inland rivers, shipping channels, and harbors
  • Ports: Terminals, piers, and facilities where cargo and passengers are transferred
  • Vessels: Commercial ships, barges, ferries, and support craft
  • Intermodal Connections: Railroads, highways, and logistics hubs that tie maritime transport into national and global supply chains

The Coast Guard plays a central role in ensuring the safety, security, and efficiency of the MTS, handling over $5.4 trillion in annual economic activity. As digital systems increasingly support operations across the MTS, from crane control to cargo tracking, cybersecurity has become essential to protecting this lifeline of U.S. trade and infrastructure.

Maritime Transportation Systems also enable international trade, making them prime targets for cyber threats from ransomware gangs to nation-state actors.

To defend against growing threats, the United States Coast Guard (USCG) has moved from encouraging cybersecurity best practices to enforcing them, culminating in a new mandate that goes into effect on July 16, 2025. These regulations aim to secure the digital backbone of the maritime industry.

Why maritime ports are at risk

Modern ports are a blend of legacy and modern OT, IoT, and IT digitally connected technologies that enable crane operations, container tracking, terminal storage, logistics, and remote maintenance.

Many of these systems were never designed with cybersecurity in mind, making them vulnerable to lateral movement and disruptive ransomware attack spillover.

The convergence of business IT networks and operational infrastructure further expands the attack surface, especially with the rise of cloud adoption and unmanaged IoT and IIoT devices.

Cyber incidents in recent years have demonstrated how ransomware or malicious activity can halt crane operations, disrupt logistics, and compromise safety at scale threatening not only port operations, but national security and economic stability.

Relevant cyber-attacks on maritime ports

Maersk & Port of Los Angeles (2017 – NotPetya):
A ransomware attack crippled A.P. Moller-Maersk, the world’s largest shipping company. Operations at 17 ports, including the Port of Los Angeles, were halted due to system outages, causing weeks of logistical chaos.

Port of San Diego (2018 – Ransomware Attack):
A ransomware attack targeted the Port of San Diego, disrupting internal IT systems including public records, business services, and dockside cargo operations. While marine traffic was unaffected, commercial activity slowed significantly during recovery.

Port of Houston (2021 – Nation-State Intrusion):
A suspected nation-state actor exploited a known vulnerability in a Port of Houston web application to gain access to its network. While the attack was reportedly thwarted, it triggered a federal investigation and highlighted the vulnerability of maritime systems.

Jawaharlal Nehru Port Trust, India (2022 – Ransomware Incident):
India’s largest container port experienced disruptions due to a ransomware attack affecting operations and logistics systems. Container handling and cargo movement slowed as IT systems were taken offline during recovery efforts.

A regulatory shift: From guidance to enforcement

Since the Maritime Transportation Security Act (MTSA) of 2002, ports have been required to develop and maintain security plans. Cybersecurity formally entered the regulatory fold in 2020 with revisions to 33 CFR Part 105 and 106, requiring port authorities to assess and address computer system vulnerabilities.

In January 2025, the USCG finalized new rules to enforce cybersecurity practices across the MTS. Key elements include (but are not limited to):

  • A dedicated cyber incident response plan (PR.IP-9)
  • Routine cybersecurity risk assessments and exercises (ID.RA)
  • Designation of a cybersecurity officer and regular workforce training (section 3.1)
  • Controls for access management, segmentation, logging, and encryption (PR.AC-1:7)
  • Supply chain risk management (ID.SC)
  • Incident reporting to the National Response Center

Port operators are encouraged to align their programs with the NIST Cybersecurity Framework (CSF 2.0) and NIST SP 800-82r3, which provide comprehensive guidance for IT and OT security in industrial environments.

How Darktrace can support maritime & ports

Unified IT + OT + Cloud coverage

Maritime ports operate in hybrid environments spanning business IT systems (finance, HR, ERP), industrial OT (cranes, gates, pumps, sensors), and an increasing array of cloud and SaaS platforms.

Darktrace is the only vendor that provides native visibility and threat detection across OT/IoT, IT, cloud, and SaaS environments — all in a single platform. This means:

  • Cranes and other physical process control networks are monitored in the same dashboard as Active Directory and Office 365.
  • Threats that start in the cloud (e.g., phishing, SaaS token theft) and pivot or attempt to pivot into OT are caught early — eliminating blind spots that siloed tools miss.

This unification is critical to meeting USCG requirements for network-wide monitoring, risk identification, and incident response.

AI that understands your environment. Not just known threats

Darktrace’s AI doesn’t rely on rules or signatures. Instead, it uses Self-Learning AI TM that builds a unique “pattern of life” for every device, protocol, user, and network segment, whether it’s a crane router or PLC, SCADA server, Workstation, or Linux file server.

  • No predefined baselines or manual training
  • Real-time anomaly detection for zero-days, ransomware, and supply chain compromise
  • Continuous adaptation to new devices, configurations, and operations

This approach is critical in diverse distributed OT environments where change and anomalous activity on the network are more frequent. It also dramatically reduces the time and expertise needed to classify and inventory assets, even for unknown or custom-built systems.

Supporting incident response requirements

A key USCG requirement is that cybersecurity plans must support effective incident response.

Key expectations include:

  • Defined response roles and procedures: Personnel must know what to do and when (RS.CO-1).
  • Timely reporting: Incidents must be reported and categorized according to established criteria (RS.CO-2, RS.AN-4).
  • Effective communication: Information must be shared internally and externally, including voluntary collaboration with law enforcement and industry peers (RS.CO-3 through RS.CO-5).
  • Thorough analysis: Alerts must be investigated, impacts understood, and forensic evidence gathered to support decision-making and recovery (RS.AN-1 through RS.AN-5).
  • Swift mitigation: Incidents must be contained and resolved efficiently, with newly discovered vulnerabilities addressed or documented (RS.MI-1 through RS.MI-3).
  • Ongoing improvement: Organizations must refine their response plans using lessons learned from past incidents (RS.IM-1 and RS.IM-2).

That means detections need to be clear, accurate, and actionable.

Darktrace cuts through the noise using AI that prioritizes only high-confidence incidents and provides natural-language narratives and investigative reports that explain:

  • What’s happening, where it’s happening, when it’s happening
  • Why it’s unusual
  • How to respond

Result: Port security teams often lean and multi-tasked can meet USCG response-time expectations and reporting needs without needing to scale headcount or triage hundreds of alerts.

Built-for-edge deployment

Maritime environments are constrained. Many traditional SaaS deployment types often are unsuitable for tugboats, cranes, or air-gapped terminal systems.

Darktrace builds and maintains its own ruggedized, purpose-built appliances and unique virtual deployment options that:

  • Deploy directly into crane networks or terminal enclosures
  • Require no configuration or tuning, drop-in ready
  • Support secure over-the-air updates and fleet management
  • Operate without cloud dependency, supporting isolated and air-gapped systems

Use case: Multiple ports have been able to deploy Darktrace directly into the crane’s switch enclosure, securing lateral movement paths without interfering with the crane control software itself.

Segmentation enforcement & real-time threat containment

Darktrace visualizes real-time connectivity and attack pathways across IT, OT, and IoT it and integrates with firewalls (e.g., Fortinet, Cisco, Palo Alto) to enforce segmentation using AI insights alongside Darktrace’s own native autonomous and human confirmed response capabilities.

Benefits of autonomous and human confirmed response:

  • Auto-isolate rogue devices before the threat can escalate
  • Quarantine a suspicious connectivity with confidence operations won’t be halted
  • Autonomously buy time for human responders during off-hours or holidays
  • This ensures segmentation isn't just documented but that in the case of its failure or exploitation responses are performed as a compensating control

No reliance on 3rd parties or external connectivity

Darktrace’s supply chain integrity is a core part of its value to critical infrastructure customers. Unlike solutions that rely on indirect data collection or third-party appliances, Darktrace:

  • Uses in-house engineered sensors and appliances
  • Does not require transmission of data to or from the cloud

This ensures confidence in both your cyber visibility and the security of the tools you deploy.

See examples here of how Darktrace stopped supply chain attacks:

Readiness for USCG and Beyond

With a self-learning system that adapts to each unique port environment, Darktrace helps maritime operators not just comply but build lasting cyber resilience in a high-threat landscape.

Cybersecurity is no longer optional for U.S. ports its operationally and nationally critical. Darktrace delivers the intelligence, automation, and precision needed to meet USCG requirements and protect the digital lifeblood of the modern 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
Daniel Simonds
Director of Operational Technology

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December 22, 2025

The Year Ahead: AI Cybersecurity Trends to Watch in 2026

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Introduction: 2026 cyber trends

Each year, we ask some of our experts to step back from the day-to-day pace of incidents, vulnerabilities, and headlines to reflect on the forces reshaping the threat landscape. The goal is simple:  to identify and share the trends we believe will matter most in the year ahead, based on the real-world challenges our customers are facing, the technology and issues our R&D teams are exploring, and our observations of how both attackers and defenders are adapting.  

In 2025, we saw generative AI and early agentic systems moving from limited pilots into more widespread adoption across enterprises. Generative AI tools became embedded in SaaS products and enterprise workflows we rely on every day, AI agents gained more access to data and systems, and we saw glimpses of how threat actors can manipulate commercial AI models for attacks. At the same time, expanding cloud and SaaS ecosystems and the increasing use of automation continued to stretch traditional security assumptions.

Looking ahead to 2026, we’re already seeing the security of AI models, agents, and the identities that power them becoming a key point of tension – and opportunity -- for both attackers and defenders. Long-standing challenges and risks such as identity, trust, data integrity, and human decision-making will not disappear, but AI and automation will increase the speed and scale of the cyber risk.  

Here's what a few of our experts believe are the trends that will shape this next phase of cybersecurity, and the realities organizations should prepare for.  

Agentic AI is the next big insider risk

In 2026, organizations may experience their first large-scale security incidents driven by agentic AI behaving in unintended ways—not necessarily due to malicious intent, but because of how easily agents can be influenced. AI agents are designed to be helpful, lack judgment, and operate without understanding context or consequence. This makes them highly efficient—and highly pliable. Unlike human insiders, agentic systems do not need to be socially engineered, coerced, or bribed. They only need to be prompted creatively, misinterpret legitimate prompts, or be vulnerable to indirect prompt injection. Without strong controls around access, scope, and behavior, agents may over-share data, misroute communications, or take actions that introduce real business risk. Securing AI adoption will increasingly depend on treating agents as first-class identities—monitored, constrained, and evaluated based on behavior, not intent.

-- Nicole Carignan, SVP of Security & AI Strategy

Prompt Injection moves from theory to front-page breach

We’ll see the first major story of an indirect prompt injection attack against companies adopting AI either through an accessible chatbot or an agentic system ingesting a hidden prompt. In practice, this may result in unauthorized data exposure or unintended malicious behavior by AI systems, such as over-sharing information, misrouting communications, or acting outside their intended scope. Recent attention on this risk—particularly in the context of AI-powered browsers and additional safety layers being introduced to guide agent behavior—highlights a growing industry awareness of the challenge.  

-- Collin Chapleau, Senior Director of Security & AI Strategy

Humans are even more outpaced, but not broken

When it comes to cyber, people aren’t failing; the system is moving faster than they can. Attackers exploit the gap between human judgment and machine-speed operations. The rise of deepfakes and emotion-driven scams that we’ve seen in the last few years reduce our ability to spot the familiar human cues we’ve been taught to look out for. Fraud now spans social platforms, encrypted chat, and instant payments in minutes. Expecting humans to be the last line of defense is unrealistic.

Defense must assume human fallibility and design accordingly. Automated provenance checks, cryptographic signatures, and dual-channel verification should precede human judgment. Training still matters, but it cannot close the gap alone. In the year ahead, we need to see more of a focus on partnership: systems that absorb risk so humans make decisions in context, not under pressure.

-- Margaret Cunningham, VP of Security & AI Strategy

AI removes the attacker bottleneck—smaller organizations feel the impact

One factor that is currently preventing more companies from breaches is a bottleneck on the attacker side: there’s not enough human hacker capital. The number of human hands on a keyboard is a rate-determining factor in the threat landscape. Further advancements of AI and automation will continue to open that bottleneck. We are already seeing that. The ostrich approach of hoping that one’s own company is too obscure to be noticed by attackers will no longer work as attacker capacity increases.  

-- Max Heinemeyer, Global Field CISO

SaaS platforms become the preferred supply chain target

Attackers have learned a simple lesson: compromising SaaS platforms can have big payouts. As a result, we’ll see more targeting of commercial off-the-shelf SaaS providers, which are often highly trusted and deeply integrated into business environments. Some of these attacks may involve software with unfamiliar brand names, but their downstream impact will be significant. In 2026, expect more breaches where attackers leverage valid credentials, APIs, or misconfigurations to bypass traditional defenses entirely.

-- Nathaniel Jones, VP of Security & AI Strategy

Increased commercialization of generative AI and AI assistants in cyber attacks

One trend we’re watching closely for 2026 is the commercialization of AI-assisted cybercrime. For example, cybercrime prompt playbooks sold on the dark web—essentially copy-and-paste frameworks that show attackers how to misuse or jailbreak AI models. It’s an evolution of what we saw in 2025, where AI lowered the barrier to entry. In 2026, those techniques become productized, scalable, and much easier to reuse.  

-- Toby Lewis, Global Head of Threat Analysis

Conclusion

Taken together, these trends underscore that the core challenges of cybersecurity are not changing dramatically -- identity, trust, data, and human decision-making still sit at the core of most incidents. What is changing quickly is the environment in which these challenges play out. AI and automation are accelerating everything: how quickly attackers can scale, how widely risk is distributed, and how easily unintended behavior can create real impact. And as technology like cloud services and SaaS platforms become even more deeply integrated into businesses, the potential attack surface continues to expand.  

Predictions are not guarantees. But the patterns emerging today suggest that 2026 will be a year where securing AI becomes inseparable from securing the business itself. The organizations that prepare now—by understanding how AI is used, how it behaves, and how it can be misused—will be best positioned to adopt these technologies with confidence in the year ahead.

Learn more about how to secure AI adoption in the enterprise without compromise by registering to join our live launch webinar on February 3, 2026.  

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December 22, 2025

Why Organizations are Moving to Label-free, Behavioral DLP for Outbound Email

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Why outbound email DLP needs reinventing

In 2025, the global average cost of a data breach fell slightly — but remains substantial at USD 4.44 million (IBM Cost of a Data Breach Report 2025). The headline figure hides a painful reality: many of these breaches stem not from sophisticated hacks, but from simple human error: mis-sent emails, accidental forwarding, or replying with the wrong attachment. Because outbound email is a common channel for sensitive data leaving an organization, the risk posed by everyday mistakes is enormous.

In 2025, 53% of data breaches involved customer PII, making it the most commonly compromised asset (IBM Cost of a Data Breach Report 2025). This makes “protection at the moment of send” essential. A single unintended disclosure can trigger compliance violations, regulatory scrutiny, and erosion of customer trust –consequences that are disproportionate to the marginal human errors that cause them.

Traditional DLP has long attempted to mitigate these impacts, but it relies heavily on perfect labelling and rigid pattern-matching. In reality, data loss rarely presents itself as a neat, well-structured pattern waiting to be caught – it looks like everyday communication, just slightly out of context.

How data loss actually happens

Most data loss comes from frustratingly familiar scenarios. A mistyped name in auto-complete sends sensitive data to the wrong “Alex.” A user forwards a document to a personal Gmail account “just this once.” Someone shares an attachment with a new or unknown correspondent without realizing how sensitive it is.

Traditional, content-centric DLP rarely catches these moments. Labels are missing or wrong. Regexes break the moment the data shifts formats. And static rules can’t interpret the context that actually matters – the sender-recipient relationship, the communication history, or whether this behavior is typical for the user.

It’s the everyday mistakes that hurt the most. The classic example: the Friday 5:58 p.m. mis-send, when auto-complete selects Martin, a former contractor, instead of Marta in Finance.

What traditional DLP approaches offer (and where gaps remain)

Most email DLP today follows two patterns, each useful but incomplete.

  • Policy- and label-centric DLP works when labels are correct — but content is often unlabeled or mislabeled, and maintaining classification adds friction. Gaps appear exactly where users move fastest
  • Rule and signature-based approaches catch known patterns but miss nuance: human error, new workflows, and “unknown unknowns” that don’t match a rule

The takeaway: Protection must combine content + behavior + explainability at send time, without depending on perfect labels.

Your technology primer: The three pillars that make outbound DLP effective

1) Label-free (vs. data classification)

Protects all content, not just what’s labeled. Label-free analysis removes classification overhead and closes gaps from missing or incorrect tags. By evaluating content and context at send time, it also catches misdelivery and other payload-free errors.

  • No labeling burden; no regex/rule maintenance
  • Works when tags are missing, wrong, or stale
  • Detects misdirected sends even when labels look right

2) Behavioral (vs. rules, signatures, threat intelligence)

Understands user behavior, not just static patterns. Behavioral analysis learns what’s normal for each person, surfacing human error and subtle exfiltration that rules can’t. It also incorporates account signals and inbound intel, extending across email and Teams.

  • Flags risk without predefined rules or IOCs
  • Catches misdelivery, unusual contacts, personal forwards, odd timing/volume
  • Blends identity and inbound context across channels

3) Proprietary DSLM (vs. generic LLM)

Optimized for precise, fast, explainable on-send decisions. A DSLM understands email/DLP semantics, avoids generative risks, and stays auditable and privacy-controlled, delivering intelligence reliably without slowing mail flow.

  • Low-latency, on-send enforcement
  • Non-generative for predictable, explainable outcomes
  • Governed model with strong privacy and auditability

The Darktrace approach to DLP

Darktrace / EMAIL – DLP stops misdelivery and sensitive data loss at send time using hold/notify/justify/release actions. It blends behavioral insight with content understanding across 35+ PII categories, protecting both labeled and unlabeled data. Every action is paired with clear explainability: AI narratives show exactly why an email was flagged, supporting analysts and helping end-users learn. Deployment aligns cleanly with existing SOC workflows through mail-flow connectors and optional Microsoft Purview label ingestion, without forcing duplicate policy-building.

Deployment is simple: Microsoft 365 routes outbound mail to Darktrace for real-time, inline decisions without regex or rule-heavy setup.

A buyer’s checklist for DLP solutions

When choosing your DLP solution, you want to be sure that it can deliver precise, explainable protection at the moment it matters – on send – without operational drag.  

To finish, we’ve compiled a handy list of questions you can ask before choosing an outbound DLP solution:

  • Can it operate label free when tags are missing or wrong? 
  • Does it truly learn per user behavior (no shortcuts)? 
  • Is there a domain specific model behind the content understanding (not a generic LLM)? 
  • Does it explain decisions to both analysts and end users? 
  • Will it integrate with your label program and SOC workflows rather than duplicate them? 

For a deep dive into Darktrace’s DLP solution, check out the full solution brief.

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About the author
Carlos Gray
Senior Product Marketing Manager, Email
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