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November 6, 2023

How PlugX Malware Has Evolved & Adapted

Discover how Darktrace effectively detected and thwarted the PlugX remote access trojan in 2023 despite its highly evasive and adaptive nature.
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
Nahisha Nobregas
SOC Analyst
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06
Nov 2023

What is PlugX Remote Access Trojan?

Understanding remote access trojans (RATs)

As malicious actors across the threat landscape continue to pursue more efficient and effective ways of compromising target networks, all while remaining undetected by security measures, it is unsurprising to see an increase in the use of remote access trojans (RATs) in recent years. RATs typically operate stealthily, evading security tools while offering threat actors remote control over infected devices, allowing attackers to execute a wide range of malicious activities like data theft or installing additional malware.

Definition and general functionality of RATs: A Remote Access Trojan (RAT) is a type of malware that enables unauthorized remote control of an infected computer. Once installed, RATs allow attackers to monitor user activities, steal sensitive information, manipulate files, and execute commands. RATs are typically distributed via phishing emails, malicious attachments, drive-by downloads, or exploiting software vulnerabilities. Due to their ability to provide comprehensive control over a compromised system, RATs pose a significant security threat to individuals and organizations.

Historical overview of PlugX

PlugX is one such example of a RAT that has attributed to Chinese threat actors such as Mustang Panda, since it first appeared in the wild back in 2008. It is known for its use in espionage, a modular and plug-in style approach to malware development. It has the ability to evolve with the latest tactics, techniques, and procedures (TTPs) that allow it to avoid the detection of traditional security tools as it implants itself target devices.

How does PlugX work?

The ultimate goal of any RAT is to remotely control affected devices with a wide range of capabilities, which in PlugX’s case has typically included rebooting systems, keylogging, managing critical system processes, and file upload/downloads. One technique PlugX heavily relies on is dynamic-link library (DLL) sideloading to infiltrate devices. This technique involves executing a malicious payload that is embedded within a benign executable found in a data link library (DLL) [1]. The embedded payload within the DLL is often encrypted or obfuscated to prevent detection.

What’s more, a new variant of PlugX was observed in the wild across Papua New Guinea, Ghana, Mongolia, Zimbabwe, and Nigeria in August 2022, that added several new capabilities to its toolbox.

Key capabilities of PlugX

The new variation is reported to continuously monitor affected environments for new USB devices to infect, allowing it to spread further through compromised networks [2]. It is then able to hide malicious files within a USB device by using a novel technique that prevents them from being viewed on Windows operating systems (OS). These hidden files can only be viewed on a Unix-like (.nix) OS, or by analyzing an affected USB devices with a forensic tool [2]. The new PlugX variant also has the ability to create a hidden directory, “RECYCLER.BIN”, containing a collection of stolen documents, likely in preparation for exfiltration via its command and control (C2) channels. [3]

Since December 2022, PlugX has been observed targeting networks in Europe through malware delivery via HTML smuggling campaigns, a technique that has been dubbed SmugX [4].

This evasive tactic allows threat actors to prepare and deploy malware via phishing campaigns by exploiting legitimate HTML5 and JavaScript features [5].

Darktrace Coverage of PlugX

Between January and March 2023, Darktrace observed activity relating to the PlugX RAT on multiple customers across the fleet. While PlugX’s TTPs may have bypassed traditional security tools, the anomaly-based detection capabilities of Darktrace allowed it to identify and alert the subtle deviations in the behavior of affected devices, while Darktrace was able to take immediate mitigative action against such anomalous activity and stop attackers in their tracks.  

C2 Communication

Between January and March 2023, Darktrace detected multiple suspicious connections related to the PlugX RAT within customer environments. When a device has been infected, it will typically communicate through C2 infrastructure established for the PlugX RAT. In most cases observed by Darktrace, affected devices exhibited suspicious C2 connections to rare endpoints that were assessed with moderate to high confidence to be linked to PlugX.

On the network of one Darktrace customer the observed communication was a mix of successful and unsuccessful connections at a high volume to rare endpoints on ports such as 110, 443, 5938, and 80. These ports are commonly associated with POP3, HTTPS, TeamViewer RDP / DynGate, and HTTP, respectively.  Figure 1 below showcases this pattern of activity.

Figure 1: Model Breach Event Log demonstrating various successful and unsuccessful connections to the PlugX C2 endpoint 103.56.53[.]46 via various destination ports.

On another customer’s network, Darktrace observed C2 communication involving multiple failed connection attempts to another rare external endpoint associated with PlugX. The device in this case was detected attempting connections to the endpoint, 45.142.166[.]112 on ports 110, 80, and 443 which caused the DETECT model ‘Anomalous Connection / Multiple Failed Connections to Rare Endpoint’ to breach. This model examines devices attempting connections to a rare external endpoint over a short period of time, and it breached in response to almost all PlugX C2 related activity detected by Darktrace. This highlights Darktrace DETECT’s unique ability to identify anomalous activity which appears benign or uncertain, rather than relying on traditional signature-based detections.

Figure 2: Device Event Log demonstrating various successful and unsuccessful connections to the PlugX C2 endpoint 45.142.166[.]112 via various destination on January 27, 2023.

New User Agent

Darktrace's Self-Learning AI approach to threat detection also allowed it to recognize connections to PlugX associated endpoints that utilized a new user agent. In almost all connections to PlugX endpoints detected by Darktrace, the same user agent, Mozilla/5.0 (Windows NT 10.0;Win64;x64)AppleWebKit/537.36, was observed, illustrating a clear pattern in PlugX-related activity

In one example from February 2023, an affected device successfully connected to an endpoint associated with PlugX, 45.142.166[.]112, while using the aforementioned new user agent, as depicted in Figure 3.

Figure 3: The Device Event log above showcases a successful connection to the PlugX associated IP address, 45.142.166[.]112 using the new user agent ‘Mozilla/5.0 (Windows NT 10.0;Win64;x64)AppleWebKit/537.36’.

On March 21, 2023, Darktrace observed similar activity on a separate customer’s network affected by connections to PlugX. This activity included connections to the same endpoint, 45.142.166[.]112. The connection was an HTTP POST request made via proxy with the same new user agent, ‘Mozilla/5.0 (Windows NT 10.0;Win64;x64)AppleWebKit/537.36’. When investigated further this user agent actually reveals very little about itself and appears to be missing a couple of common features that are typically contained in a user agent string, such as a web browser and its version or the mention of Safari before its build ID (‘537.36’).

Additionally, for this connection the URI observed consisted of a random string of 8 hexadecimal characters, namely ‘d819f07a’. This is a technique often used by malware to communicate with its C2 servers, while evading the detection of signature-based detection tools. Darktrace, however, recognized that this external connection to an endpoint with no hostname constituted anomalous behavior, and could have been indicative of a threat actor communicating with malicious infrastructure, thus the ‘Anomalous Connection / Possible Callback URI’ model was breached.

Figure 4: An affected device was detected using the new user agent, ‘Mozilla/5.0 (Windows NT 10.0;Win64;x64)AppleWebKit/537.36’ while connecting to the rare external endpoint 45.142.166[.]112 via proxy.

Numeric File Download

Darktrace’s detection of PlugX activity on another customer’s network, in February 2023, helped to demonstrate related patterns of activity within the C2 communication and tooling attack phases. Observed PlugX activity on this network followed the subsequent pattern; a connection to a PlugX endpoints is made, followed by a HTTP POST request to a numeric URI with a random string of 8 hexadecimal characters, as previously highlighted. Darktrace identified that this activity represented unusual ‘New Activity’ for this device, and thus treated it with suspicion.

Figure 5: New activity was identified by Darktrace in the Device Event Log shown above for connections to the endpoint 45.142.166[.]112 followed by HTTP POSTs to URIs “/8891431c” and “/ba12b866” on February 15, 2023.

The device in question continued to connect to the endpoint and make HTTP POST connections to various URIs relating to PlugX. Additionally, the user agent `Mozilla/5.0 (Windows NT 10.0;Win64;x64)AppleWebKit/537.36` was again detected for these connections. Figure 6 details the activity captured by Darktrace’s Cyber AI Analyst.

Figure 6: The image above showcases activity captured by Darktrace’s AI Analyst for PlugX connections made on February 15, 2023.

Darktrace detected that during these connections, the device in question attempted to download a suspicious file named only with numbers. The use of numeric file names is a technique often used by threat actors to obfuscate the download of malicious files or programs and bypass traditional security tools. Darktrace understood that the download of a numeric file, coupled with the use of an anomalous new user agent, mean the incident should be treated with suspicion. Fortunately, Darktrace RESPOND was enabled in autonomous response mode during this attack, meaning it was able to automatically block the device from downloading the file, or any other files, from the suspicious external location for a two-hour period, potentially preventing the download of PlugX’s malicious tooling.

Conclusion

Amid the continued evolution of PlugX from an espionage tool to a more widely available malware, it is essential that threat detection does not rely on a set of characteristics or indicators, but rather is focused on anomalies. Throughout these cases, Darktrace demonstrated the efficacy of its detection and alerting on emerging activity pertaining to a particularly stealthy and versatile RAT. Over the years, PlugX has continually looked to evolve and survive in the ever-changing threat landscape by adapting new capabilities and TTPs through which it can infect a system and spread to new devices without being noticed by security teams and their tools.

However, Darktrace’s Self-Learning AI allows it to gain a strong understanding of customer networks, learning what constitutes expected network behavior which in turn allows it to recognize the subtle deviations indicative of an ongoing compromise.

Darktrace’s ability to identify emerging threats through anomaly-based detection, rather than relying on established threat intelligence, uniquely positions it to detect and respond to highly adaptable and dynamic threats, like the PlugX malware, regardless of how it may evolve in the future.

Credit to: Nahisha Nobregas, SOC Analyst & Dylan Hinz, Cyber Analyst

Appendices

MITRE ATT&CK Framework

Execution

  • T1059.003 Command and Scripting Interpreter: Windows Command Shell

Persistence and Privilege Escalation

  • T1547.001 Boot or Logon AutoStart Execution: Registry Run Keys / Startup Folder
  • T1574.001 Hijack Execution Flow: DLL Search Order Hijacking
  • T1574.002 Hijack Execution Flow: DLL Side-Loading
  • T1543.003 Create or Modify System Process: Windows Service
  • T1140 Deobfuscate / Decode Files or Information
  • T1083 File and Directory Discovery

Defense Evasion

  • T1564.001 Hide Artifacts: Hidden Files and Directories
  • T1036.004 Masquerading: Task or Service
  • T1036.005 Masquerading: Match Legitimate Name or Location
  • T1027.006 Obfuscated Files or Information: HTML Smuggling

Credential Access

  • T1056.001 Input Capture: Keylogging

Collection

  • T1105 Ingress Tool Transfer

Command and Control

  • T1573.001 Encrypted Channel: Symmetric Cryptography
  • T1070.003 Mail Protocols
  • T1071.001 Web Protocol

DETECT Model Breaches

  • Anomalous Connection / Multiple Failed Connections to Rare Endpoint
  • Anomalous Connection / New User Agent to IP Without Hostname
  • Anomalous File / New User Agent Followed By Numeric File Download
  • Anomalous Connection / Possible Callback URL

Indicators of Compromise (IoCs)

IoC - Type - Description + Confidence

45.142.166[.]112 - IP - PlugX C2 Endpoint / moderate - high

103.56.53[.]46 - IP - PlugX C2 Endpoint / moderate - high

Mozilla/5.0 (Windows NT 10.0;Win64;x64)AppleWebKit/537.36 - User Agent - PlugX User Agent / moderate – high

/8891431c - URI - PlugX URI / moderate-high

/ba12b866 - URI - PlugX URI / moderate -high

References

1. https://www.crowdstrike.com/blog/dll-side-loading-how-to-combat-threat-actor-evasion-techniques/

2. https://unit42.paloaltonetworks.com/plugx-variants-in-usbs/

3. https://news.sophos.com/en-us/2023/03/09/border-hopping-plugx-usb-worm/

4. https://thehackernews.com/2023/07/chinese-hackers-use-html-smuggling-to.html

5. https://www.cyfirma.com/outofband/html-smuggling-a-stealthier-approach-to-deliver-malware/

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
Nahisha Nobregas
SOC Analyst

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December 23, 2025

How to Secure AI in the Enterprise: A Practical Framework for Models, Data, and Agents

How to secure AI in the enterprise: A practical framework for models, data, and agents Default blog imageDefault blog image

Introduction: Why securing AI is now a security priority

AI adoption is at the forefront of the digital movement in businesses, outpacing the rate at which IT and security professionals can set up governance models and security parameters. Adopting Generative AI chatbots, autonomous agents, and AI-enabled SaaS tools promises efficiency and speed but also introduces new forms of risk that traditional security controls were never designed to manage. For many organizations, the first challenge is not whether AI should be secured, but what “securing AI” actually means in practice. Is it about protecting models? Governing data? Monitoring outputs? Or controlling how AI agents behave once deployed?  

While demand for adoption increases, securing AI use in the enterprise is still an abstract concept to many and operationalizing its use goes far beyond just having visibility. Practitioners need to also consider how AI is sourced, built, deployed, used, and governed across the enterprise.

The goal for security teams: Implement a clear, lifecycle-based AI security framework. This blog will demonstrate the variety of AI use cases that should be considered when developing this framework and how to frame this conversation to non-technical audiences.  

What does “securing AI” actually mean?

Securing AI is often framed as an extension of existing security disciplines. In practice, this assumption can cause confusion.

Traditional security functions are built around relatively stable boundaries. Application security focuses on code and logic. Cloud security governs infrastructure and identity. Data security protects sensitive information at rest and in motion. Identity security controls who can access systems and services. Each function has clear ownership, established tooling, and well-understood failure modes.

AI does not fit neatly into any of these categories. An AI system is simultaneously:

  • An application that executes logic
  • A data processor that ingests and generates sensitive information
  • A decision-making layer that influences or automates actions
  • A dynamic system that changes behavior over time

As a result, the security risks introduced by AI cuts across multiple domains at once. A single AI interaction can involve identity misuse, data exposure, application logic abuse, and supply chain risk all within the same workflow. This is where the traditional lines between security functions begin to blur.

For example, a malicious prompt submitted by an authorized user is not a classic identity breach, yet it can trigger data leakage or unauthorized actions. An AI agent calling an external service may appear as legitimate application behavior, even as it violates data sovereignty or compliance requirements. AI-generated code may pass standard development checks while introducing subtle vulnerabilities or compromised dependencies.

In each case, no single security team “owns” the risk outright.

This is why securing AI cannot be reduced to model safety, governance policies, or perimeter controls alone. It requires a shared security lens that spans development, operations, data handling, and user interaction. Securing AI means understanding not just whether systems are accessed securely, but whether they are being used, trained, and allowed to act in ways that align with business intent and risk tolerance.

At its core, securing AI is about restoring clarity in environments where accountability can quickly blur. It is about knowing where AI exists, how it behaves, what it is allowed to do, and how its decisions affect the wider enterprise. Without this clarity, AI becomes a force multiplier for both productivity and risk.

The five categories of AI risk in the enterprise

A practical way to approach AI security is to organize risk around how AI is used and where it operates. The framework below defines five categories of AI risk, each aligned to a distinct layer of the enterprise AI ecosystem  

How to Secure AI in the Enterprise:

  • Defending against misuse and emergent behaviors
  • Monitoring and controlling AI in operation
  • Protecting AI development and infrastructure
  • Securing the AI supply chain
  • Strengthening readiness and oversight

Together, these categories provide a structured lens for understanding how AI risk manifests and where security teams should focus their efforts.

1. Defending against misuse and emergent AI behaviors

Generative AI systems and agents can be manipulated in ways that bypass traditional controls. Even when access is authorized, AI can be misused, repurposed, or influenced through carefully crafted prompts and interactions.

Key risks include:

  • Malicious prompt injection designed to coerce unwanted actions
  • Unauthorized or unintended use cases that bypass guardrails
  • Exposure of sensitive data through prompt histories
  • Hallucinated or malicious outputs that influence human behavior

Unlike traditional applications, AI systems can produce harmful outcomes without being explicitly compromised. Securing this layer requires monitoring intent, not just access. Security teams need visibility into how AI systems are being prompted, how outputs are consumed, and whether usage aligns with approved business purposes

2. Monitoring and controlling AI in operation

Once deployed, AI agents operate at machine speed and scale. They can initiate actions, exchange data, and interact with other systems with little human oversight. This makes runtime visibility critical.

Operational AI risks include:

  • Agents using permissions in unintended ways
  • Uncontrolled outbound connections to external services or agents
  • Loss of forensic visibility into ephemeral AI components
  • Non-compliant data transmission across jurisdictions

Securing AI in operation requires real-time monitoring of agent behavior, centralized control points such as AI gateways, and the ability to capture agent state for investigation. Without these capabilities, security teams may be blind to how AI systems behave once live, particularly in cloud-native or regulated environments.

3. Protecting AI development and infrastructure

Many AI risks are introduced long before deployment. Development pipelines, infrastructure configurations, and architectural decisions all influence the security posture of AI systems.

Common risks include:

  • Misconfigured permissions and guardrails
  • Insecure or overly complex agent architectures
  • Infrastructure-as-Code introducing silent misconfigurations
  • Vulnerabilities in AI-generated code and dependencies

AI-generated code adds a new dimension of risk, as hallucinated packages or insecure logic may be harder to detect and debug than human-written code. Securing AI development means applying security controls early, including static analysis, architectural review, and continuous configuration monitoring throughout the build process.

4. Securing the AI supply chain

AI supply chains are often opaque. Models, datasets, dependencies, and services may come from third parties with varying levels of transparency and assurance.

Key supply chain risks include:

  • Shadow AI tools used outside approved controls
  • External AI agents granted internal access
  • Suppliers applying AI to enterprise data without disclosure
  • Compromised models, training data, or dependencies

Securing the AI supply chain requires discovering where AI is used, validating the provenance and licensing of models and data, and assessing how suppliers process and protect enterprise information. Without this visibility, organizations risk data leakage, regulatory exposure, and downstream compromise through trusted integrations.

5. Strengthening readiness and oversight

Even with strong technical controls, AI security fails without governance, testing, and trained teams. AI introduces new incident scenarios that many security teams are not yet prepared to handle.

Oversight risks include:

  • Lack of meaningful AI risk reporting
  • Untested AI systems in production
  • Security teams untrained in AI-specific threats

Organizations need AI-aware reporting, red and purple team exercises that include AI systems, and ongoing training to build operational readiness. These capabilities ensure AI risks are understood, tested, and continuously improved, rather than discovered during a live incident.

Reframing AI security for the boardroom

AI security is not just a technical issue. It is a trust, accountability, and resilience issue. Boards want assurance that AI-driven decisions are reliable, explainable, and protected from tampering.

Effective communication with leadership focuses on:

  • Trust: confidence in data integrity, model behavior, and outputs
  • Accountability: clear ownership across teams and suppliers
  • Resilience: the ability to operate, audit, and adapt under attack or regulation

Mapping AI security efforts to recognized frameworks such as ISO/IEC 42001 and the NIST AI Risk Management Framework helps demonstrate maturity and aligns AI security with broader governance objectives.

Conclusion: Securing AI is a lifecycle challenge

The same characteristics that make AI transformative also make it difficult to secure. AI systems blur traditional boundaries between software, users, and decision-making, expanding the attack surface in subtle but significant ways.

Securing AI requires restoring clarity. Knowing where AI exists, how it behaves, who controls it, and how it is governed. A framework-based approach allows organizations to innovate with AI while maintaining trust, accountability, and control.

The journey to secure AI is ongoing, but it begins with understanding the risks across the full AI lifecycle and building security practices that evolve alongside the technology.

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About the author
Brittany Woodsmall
Product Marketing Manager, AI & Attack Surface

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December 22, 2025

The Year Ahead: AI Cybersecurity Trends to Watch in 2026

2026 cyber threat trendsDefault blog imageDefault blog image

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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