July 26, 2022

Identifying PrivateLoader Network Threats

Learn how Darktrace identifies network-based indicators of compromise for the PrivateLoader malware. Gain insights into advanced threat detection.

Written by

Sam Lister

Specialist Security Researcher

Written by

Shuh Chin Goh

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

Sam Lister

Specialist Security Researcher

Written by

Shuh Chin Goh

Jul 2022

Instead of delivering their malicious payloads themselves, threat actors can pay certain cybercriminals (known as pay-per-install (PPI) providers) to deliver their payloads for them. Since January 2022, Darktrace’s SOC has observed several cases of PPI providers delivering their clients’ payloads using a modular malware downloader known as ‘PrivateLoader’.

This blog will explore how these PPI providers installed PrivateLoader onto systems and outline the steps which the infected PrivateLoader bots took to install further malicious payloads. The details provided here are intended to provide insight into the operations of PrivateLoader and to assist security teams in identifying PrivateLoader bots within their own networks.

Threat Summary

Between January and June 2022, Darktrace identified the following sequence of network behaviours within the environments of several Darktrace clients. Patterns of activity involving these steps are paradigmatic examples of PrivateLoader activity:

1. A victim’s device is redirected to a page which instructs them to download a password-protected archive file from a file storage service — typically Discord Content Delivery Network (CDN)

2. The device contacts a file storage service (typically Discord CDN) via SSL connections

3. The device either contacts Pastebin via SSL connections, makes an HTTP GET request with the URI string ‘/server.txt’ or ‘server_p.txt’ to 45.144.225[.]57, or makes an HTTP GET request with the URI string ‘/proxies.txt’ to 212.193.30[.]45

4. The device makes an HTTP GET request with the URI string ‘/base/api/statistics.php’ to either 212.193.30[.]21, 85.202.169[.]116, 2.56.56[.]126 or 2.56.59[.]42

5. The device contacts a file storage service (typically Discord CDN) via SSL connections

6. The device makes a HTTP POST request with the URI string ‘/base/api/getData.php’ to either 212.193.30[.]21, 85.202.169[.]116, 2.56.56[.]126 or 2.56.59[.]42

7. The device finally downloads malicious payloads from a variety of endpoints

The PPI Business

Before exploring PrivateLoader in more detail, the pay-per-install (PPI) business should be contextualized. This consists of two parties:

1. PPI clients - actors who want their malicious payloads to be installed onto a large number of target systems. PPI clients are typically entry-level threat actors who seek to widely distribute commodity malware [1]

2. PPI providers - actors who PPI clients can pay to install their malicious payloads

As the smugglers of the cybercriminal world, PPI providers typically advertise their malware delivery services on underground web forums. In some cases, PPI services can even be accessed via Clearnet websites such as InstallBest and InstallShop [2] (Figure 1).

Figure 1: A snapshot of the InstallBest PPI login page [2]

To utilize a PPI provider’s service, a PPI client must typically specify:

(A) the URLs of the payloads which they want to be installed

(B) the number of systems onto which they want their payloads to be installed

Payment of course, is also required. To fulfil their clients’ requests, PPI providers typically make use of downloaders - malware which instructs the devices on which it is running to download and execute further payloads. PPI providers seek to install their downloaders onto as many systems as possible. Follow-on payloads are usually determined by system information garnered and relayed back to the PPI providers’ command and control (C2) infrastructure. PPI providers may disseminate their downloaders themselves, or they may outsource the dissemination to third parties called ‘affiliates’ [3].

Back in May 2021, Intel 471 researchers became aware of PPI providers using a novel downloader (dubbed ‘PrivateLoader’) to conduct their operations. Since Intel 471’s public disclosure of the downloader back in Feb 2022 [4], several other threat research teams, such as the Walmart Cyber Intel Team [5], Zscaler ThreatLabz [6], and Trend Micro Research [7] have all provided valuable insights into the downloader’s behaviour.

Anatomy of a PrivateLoader Infection

The PrivateLoader downloader, which is written in C++, was originally monolithic (i.e, consisted of only one module). At some point, however, the downloader became modular (i.e, consisting of multiple modules). The modules communicate via HTTP and employ various anti-analysis methods. PrivateLoader currently consists of the following three modules [8]:

The loader module: Instructs the system on which it is running to retrieve the IP address of the main C2 server and to download and execute the PrivateLoader core module
The core module: Instructs the system on which it is running to send system information to the main C2 server, to download and execute further malicious payloads, and to relay information regarding installed payloads back to the main C2 server
The service module: Instructs the system on which it is running to keep the PrivateLoader modules running

Kill Chain Deep-Dive

The chain of activity starts with the user’s browser being redirected to a webpage which instructs them to download a password-protected archive file from a file storage service such as Discord CDN. Discord is a popular VoIP and instant messaging service, and Discord CDN is the service’s CDN infrastructure. In several cases, the webpages to which users’ browsers were redirected were hosted on ‘hero-files[.]com’ (Figure 2), ‘qd-files[.]com’, and ‘pu-file[.]com’ (Figure 3).

Figure 2: An image of a page hosted on hero-files[.]com - an endpoint which Darktrace observed systems contacting before downloading PrivateLoader from Discord CDN

Figure 3: An image of a page hosted on pu-file[.]com- an endpoint which Darktrace observed systems contacting before downloading PrivateLoader from Discord CDN

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On attempting to download cracked/pirated software, users’ browsers were typically redirected to download instruction pages. In one case however, a user’s device showed signs of being infected with the malicious Chrome extension, ChromeBack [9], immediately before it contacted a webpage providing download instructions (Figure 4). This may suggest that cracked software downloads are not the only cause of users’ browsers being redirected to these download instruction pages (Figure 5).

Figure 4: The event log for this device (taken from the Darktrace Threat Visualiser interface) shows that the device contacted endpoints associated with ChromeBack ('freychang[.]fun') prior to visiting a page ('qd-file[.]com') which instructed the device’s user to download an archive file from Discord CDN

Figure 5: An image of the website 'crackright[.]com'- a provider of cracked software. Systems which attempted to download software from this website were subsequently led to pages providing instructions to download a password-protected archive from Discord CDN

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After users’ devices were redirected to pages instructing them to download a password-protected archive, they subsequently contacted cdn.discordapp[.]com over SSL. The archive files which users downloaded over these SSL connections likely contained the PrivateLoader loader module. Immediately after contacting the file storage endpoint, users’ devices were observed either contacting Pastebin over SSL, making an HTTP GET request with the URI string ‘/server.txt’ or ‘server_p.txt’ to 45.144.225[.]57, or making an HTTP GET request with the URI string ‘/proxies.txt’ to 212.193.30[.]45 (Figure 6).

Distinctive user-agent strings such as those containing question marks (e.g. ‘????ll’) and strings referencing outdated Chrome browser versions were consistently seen in these HTTP requests. The following chrome agent was repeatedly observed: ‘Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/74.0.3729.169 Safari/537.36’.

In some cases, devices also displayed signs of infection with other strains of malware such as the RedLine infostealer and the BeamWinHTTP malware downloader. This may suggest that the password-protected archives embedded several payloads.

Figure 6: This figure, obtained from Darktrace's Advanced Search interface, represents the post-infection behaviour displayed by a PrivateLoader bot. After visiting hero-files[.]com and downloading the PrivateLoader loader module from Discord CDN, the device can be seen making HTTP GET requests for ‘/proxies.txt’ and ‘/server.txt’ and contacting pastebin[.]com

It seems that PrivateLoader bots contact Pastebin, 45.144.225[.]57, and 212.193.30[.]45 in order to retrieve the IP address of PrivateLoader’s main C2 server - the server which provides PrivateLoader bots with payload URLs. This technique used by the operators of PrivateLoader closely mirrors the well-known espionage tactic known as ‘dead drop’.

The dead drop is a method of espionage tradecraft in which an individual leaves a physical object such as papers, cash, or weapons in an agreed hiding spot so that the intended recipient can retrieve the object later on without having to come in to contact with the source. When threat actors host information about core C2 infrastructure on intermediary endpoints, the hosted information is analogously called a ‘Dead Drop Resolver’ or ‘DDR’. Example URLs of DDRs used by PrivateLoader:

https://pastebin[.]com/...
http://212.193.30[.]45/proxies.txt
http://45.144.225[.]57/server.txt
http://45.144.255[.]57/server_p.txt

The ‘proxies.txt’ DDR hosted on 212.193.40[.]45 contains a list of 132 IP address / port pairs. The 119^th line of this list includes a scrambled version of the IP address of PrivateLoader’s main C2 server (Figures 7 & 8). Prior to June, it seems that the main C2 IP address was ‘212.193.30[.]21’, however, the IP address appears to have recently changed to ‘85.202.169[.]116’. In a limited set of cases, Darktrace also observed PrivateLoader bots retrieving payload URLs from 2.56.56[.]126 and 2.56.59[.]42 (rather than from 212.193.30[.]21 or 85.202.169[.]116). These IP addresses may be hardcoded secondary C2 address which PrivateLoader bots use in cases where they are unable to retrieve the primary C2 address from Pastebin, 212.193.30[.]45 or 45.144.255[.]57 [10].

Figure 7: Before June, the 119th entry of the ‘proxies.txt’ file lists '30.212.21.193' - a scrambling of the ‘212.193.30[.]21’ main C2 IP address

Figure 8: Since June, the 119th entry of the ‘proxies.txt’ file lists '169.85.116.202' - a scrambling of the '85.202.169[.]116' main C2 IP address

Once PrivateLoader bots had retrieved C2 information from either Pastebin, 45.144.225[.]57, or 212.193.30[.]45, they went on to make HTTP GET requests for ‘/base/api/statistics.php’ to either 212.193.30[.]21, 85.202.169[.]116, 2.56.56[.]126, or 2.56.59[.]42 (Figure 9). The server responded to these requests with an XOR encrypted string. The strings were encrypted using a 1-byte key [11], such as 0001101 (Figure 10). Decrypting the string revealed a URL for a BMP file hosted on Discord CDN, such as ‘hxxps://cdn.discordapp[.]com/attachments/978284851323088960/986671030670078012/PL_Client.bmp’. These encrypted URLs appear to be file download paths for the PrivateLoader core module.

Figure 9: HTTP response from server to an HTTP GET request for '/base/api/statistics.php'

Figure 10: XOR decrypting the string with the one-byte key, 00011101, outputs a URL in CyberChef

After PrivateLoader bots retrieved the 'cdn.discordapp[.]com’ URL from 212.193.30[.]21, 85.202.169[.]116, 2.56.56[.]126, or 2.56.59[.]42, they immediately contacted Discord CDN via SSL connections in order to obtain the PrivateLoader core module. Execution of this module resulted in the bots making HTTP POST requests (with the URI string ‘/base/api/getData.php’) to the main C2 address (Figures 11 & 12). Both the data which the PrivateLoader bots sent over these HTTP POST requests and the data returned via the C2 server’s HTTP responses were heavily encrypted using a combination of password-based key derivation, base64 encoding, AES encryption, and HMAC validation [12].

Figure 11: The above image, taken from Darktrace's Advanced Search interface, shows a PrivateLoader bot carrying out the following steps: contact ‘hero-files[.]com’ --> contact ‘cdn.discordapp[.]com’ --> retrieve ‘/proxies.txt’ from 212.193.30[.]45 --> retrieve ‘/base/api/statistics.php’ from 212.193.30[.]21 --> contact ‘cdn.discordapp[.]com --> make HTTP POST request with the URI ‘base/api/getData.php’ to 212.193.30[.]21

Figure 12: A PCAP of the data sent via the HTTP POST (in red), and the data returned by the C2 endpoint (in blue)

These ‘/base/api/getData.php’ POST requests contain a command, a campaign name and a JSON object. The response may either contain a simple status message (such as “success”) or a JSON object containing URLs of payloads. After making these HTTP connections, PrivateLoader bots were observed downloading and executing large volumes of payloads (Figure 13), ranging from crypto-miners to infostealers (such as Mars stealer), and even to other malware downloaders (such as SmokeLoader). In some cases, bots were also seen downloading files with ‘.bmp’ extensions, such as ‘Service.bmp’, ‘Cube_WW14.bmp’, and ‘NiceProcessX64.bmp’, from 45.144.225[.]57 - the same DDR endpoint from which PrivateLoader bots retrieved main C2 information. These ‘.bmp’ payloads are likely related to the PrivateLoader service module [13]. Certain bots made follow-up HTTP POST requests (with the URI string ‘/service/communication.php’) to either 212.193.30[.]21 or 85.202.169[.]116, indicating the presence of the PrivateLoader service module, which has the purpose of establishing persistence on the device (Figure 14).

Figure 13: The above image, taken from Darktrace's Advanced Search interface, outlines the plethora of malware payloads downloaded by a PrivateLoader bot after it made an HTTP POST request to the ‘/base/api/getData.php’ endpoint. The PrivateLoader service module is highlighted in red

Figure 14: The event log for a PrivateLoader bot, obtained from the Threat Visualiser interface, shows a device making HTTP POST requests to ‘/service/communication.php’ and connecting to the NanoPool mining pool, indicating successful execution of downloaded payloads

In several observed cases, PrivateLoader bots downloaded another malware downloader called ‘SmokeLoader’ (payloads named ‘toolspab2.exe’ and ‘toolspab3.exe’) from “Privacy Tools” endpoints [14], such as ‘privacy-tools-for-you-802[.]com’ and ‘privacy-tools-for-you-783[.]com’. These “Privacy Tools” domains are likely impersonation attempts of the legitimate ‘privacytools[.]io’ website - a website run by volunteers who advocate for data privacy [15].

After downloading and executing malicious payloads, PrivateLoader bots were typically seen contacting crypto-mining pools, such as NanoPool, and making HTTP POST requests to external hosts associated with SmokeLoader, such as hosts named ‘host-data-coin-11[.]com’ and ‘file-coin-host-12[.]com’ [16]. In one case, a PrivateLoader bot went on to exfiltrate data over HTTP to an external host named ‘cheapf[.]link’, which was registered on the 14^th March 2022 [17]. The name of the file which the PrivateLoader bot used to exfiltrate data was ‘NOP8QIMGV3W47Y.zip’, indicating information stealing activities by Mars Stealer (Figure 15) [18]. By saving the HTTP stream as raw data and utilizing a hex editor to remove the HTTP header portions, the hex data of the ZIP file was obtained. Saving the hex data using a ‘.zip’ extension and extracting the contents, a file directory consisting of system information and Chrome and Edge browsers’ Autofill data in cleartext .txt file format could be seen (Figure 16).

Figure 15: A PCAP of a PrivateLoader bot’s HTTP POST request to cheapf[.]link, with data sent by the bot appearing to include Chrome and Edge autofill data, as well as system information

Figure 16: File directory structure and files of the ZIP archive

When left unattended, PrivateLoader bots continued to contact C2 infrastructure in order to relay details of executed payloads and to retrieve URLs of further payloads.

Darktrace Coverage

Most of the incidents surveyed for this article belonged to prospective customers who were trialling Darktrace with RESPOND in passive mode, and thus without the ability for autonomous intervention. However in all observed cases, Darktrace DETECT was able to provide visibility into the actions taken by PrivateLoader bots. In one case, despite the infected bot being disconnected from the client’s network, Darktrace was still able to provide visibility into the device’s network behaviour due to the client’s usage of Darktrace/Endpoint.

If a system within an organization’s network becomes infected with PrivateLoader, it will display a range of anomalous network behaviours before it downloads and executes malicious payloads. For example, it will contact Pastebin or make HTTP requests with new and unusual user-agent strings to rare external endpoints. These network behaviours will generate some of the following alerts on the Darktrace UI:

Compliance / Pastebin
Device / New User Agent and New IP
Device / New User Agent
Device / Three or More New User Agents
Anomalous Connection / New User Agent to IP Without Hostname
Anomalous Connection / POST to PHP on New External Host
Anomalous Connection / Posting HTTP to IP Without Hostname

Once the infected host obtains URLs for malware payloads from a C2 endpoint, it will likely start to download and execute large volumes of malicious files. These file downloads will usually cause Darktrace to generate some of the following alerts:

Anomalous File / EXE from Rare External Location
Anomalous File / Numeric Exe Download
Anomalous File / Masqueraded File Transfer
Anomalous File / Multiple EXE from Rare External Locations
Device / Initial Breach Chain Compromise

If RESPOND is deployed in active mode, Darktrace will be able to autonomously block the download of additional malware payloads onto the target machine and the subsequent beaconing or crypto-mining activities through network inhibitors such as ‘Block matching connections’, ‘Enforce pattern of life’ and ‘Block all outgoing traffic’. The ‘Enforce pattern of life’ action results in a device only being able to make connections and data transfers which Darktrace considers normal for that device. The ‘Block all outgoing traffic’ action will cause all traffic originating from the device to be blocked. If the customer has Darktrace’s Proactive Threat Notification (PTN) service, then a breach of an Enhanced Monitoring model such as ‘Device / Initial Breach Chain Compromise’ will result in a Darktrace SOC analyst proactively notifying the customer of the suspicious activity. Below is a list of Darktrace RESPOND (Antigena) models which would be expected to breach due to PrivateLoader activity. Such models can seriously hamper attempts made by PrivateLoader bots to download malicious payloads.

Antigena / Network / External Threat / Antigena Suspicious File Block
Antigena / Network / Significant Anomaly / Antigena Controlled and Model Breach
Antigena / Network / External Threat / Antigena File then New Outbound Block
Antigena / Network / Significant Anomaly / Antigena Significant Anomaly from Client Block
Antigena / Network / Significant Anomaly / Antigena Breaches Over Time Block

In one observed case, the infected bot began to download malicious payloads within one minute of becoming infected with PrivateLoader. Since RESPOND was correctly configured, it was able to immediately intervene by autonomously enforcing the device’s pattern of life for 2 hours and blocking all of the device’s outgoing traffic for 10 minutes (Figure 17). When malware moves at such a fast pace, the availability of autonomous response technology, which can respond immediately to detected threats, is key for the prevention of further damage.

Figure 18: The event log for a Darktrace RESPOND (Antigena) model breach shows Darktrace RESPOND performing inhibitive actions once the PrivateLoader bot begins to download payloads

Conclusion

By investigating PrivateLoader infections over the past couple of months, Darktrace has observed PrivateLoader operators making changes to the downloader’s main C2 IP address and to the user-agent strings which the downloader uses in its C2 communications. It is relatively easy for the operators of PrivateLoader to change these superficial network-based features of the malware in order to evade detection [19]. However, once a system becomes infected with PrivateLoader, it will inevitably start to display anomalous patterns of network behaviour characteristic of the Tactics, Techniques and Procedures (TTPs) discussed in this blog.

Throughout 2022, Darktrace observed overlapping patterns of network activity within the environments of several customers, which reveal the archetypal steps of a PrivateLoader infection. Despite the changes made to PrivateLoader’s network-based features, Darktrace’s Self-Learning AI was able to continually identify infected bots, detecting every stage of an infection without relying on known indicators of compromise. When configured, RESPOND was able to immediately respond to such infections, preventing further advancement in the cyber kill chain and ultimately preventing the delivery of floods of payloads onto infected devices.

IoCs

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MITRE ATT&CK Techniques Observed

References

[1], [8],[13] https://www.youtube.com/watch?v=Ldp7eESQotM

[2] https://news.sophos.com/en-us/2021/09/01/fake-pirated-software-sites-serve-up-malware-droppers-as-a-service/

[3] https://www.researchgate.net/publication/228873118_Measuring_Pay-per Install_The_Commoditization_of_Malware_Distribution

[4], [15] https://intel471.com/blog/privateloader-malware

[5] https://medium.com/walmartglobaltech/privateloader-to-anubis-loader-55d066a2653e

[6], [10],[11], [12] https://www.zscaler.com/blogs/security-research/peeking-privateloader

[7] https://www.trendmicro.com/en_us/research/22/e/netdooka-framework-distributed-via-privateloader-ppi.html

[9] https://www.gosecure.net/blog/2022/02/10/malicious-chrome-browser-extension-exposed-chromeback-leverages-silent-extension-loading/

[14] https://www.proofpoint.com/us/blog/threat-insight/malware-masquerades-privacy-tool

[16] https://asec.ahnlab.com/en/30513/

[17]https://twitter.com/0xrb/status/1515956690642161669

[18] https://isc.sans.edu/forums/diary/Arkei+Variants+From+Vidar+to+Mars+Stealer/28468

[19] http://detect-respond.blogspot.com/2013/03/the-pyramid-of-pain.html

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Written by

Sam Lister

Specialist Security Researcher

Written by

Shuh Chin Goh

Inside the SOC

Written by

Sam Lister

Specialist Security Researcher

Written by

Shuh Chin Goh

Product Marketing Manager, AI & Attack Surface

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December 23, 2025

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

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.

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

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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The Year Ahead: AI Cybersecurity Trends to Watch in 2026

Introduction: 2026 cyber trends

Agentic AI is the next big insider risk

Prompt Injection moves from theory to front-page breach

Humans are even more outpaced, but not broken

‍AI removes the attacker bottleneck—smaller organizations feel the impact

SaaS platforms become the preferred supply chain target

Increased commercialization of generative AI and AI assistants in cyber attacks

Conclusion