November 5, 2025

Tracking a Dragon: Investigating a DragonForce-affiliated ransomware attack with Darktrace

Darktrace investigates a DragonForce-affiliated ransomware attack targeting the manufacturing sector. The blog details the attack lifecycle, from network scanning and credential brute-forcing to data exfiltration and file encryption.

Written by

Justin Torres

Cyber Analyst

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

Justin Torres

Cyber Analyst

Tracking a Dragon: Investigating a DragonForce-affiliated ransomware attack with Darktrace

Nov 2025

What is DragonForce?

DragonForce is a Ransomware-as-a-Service (RaaS) platform that emerged in late 2023, offering broad-scale capabilities and infrastructure to threat actors. Recently, DragonForce has been linked to attacks targeting the UK retail sector, resulting in several high-profile cases [1][2]. Moreover, the group launched an affiliate program offering a revenue share of roughly 20%, significantly lower than commissions reported across other RaaS platforms [3].

This Darktrace case study examines a DragonForce-linked RaaS infection within the manufacturing industry. The earliest signs of compromise were observed during working hours in August 2025, where an infected device started performing network scans and attempted to brute-force administrative credentials. After eight days of inactivity, threat actors returned and multiple devices began encrypting files via the SMB protocol using a DragonForce-associated file extension. Ransom notes referencing the group were also dropped, suggesting the threat actor is claiming affiliation with DragonForce, though this has not been confirmed.

Despite Darktrace’s detection of the attack in its early stages, the customer’s deployment did not have Darktrace’s Autonomous Response capability configured, allowing the threat to progress to data exfiltration and file encryption.

Darktrace's Observations

While the initial access vector was not clearly defined in this case study, it was likely achieved through common methods previously employed out by DragonForce affiliates. These include phishing emails leveraging social engineering tactics, exploitation of public-facing applications with known vulnerabilities, web shells, and/or the abuse of remote management tools.

Darktrace’s analysis identified internal devices performing internal network scanning, brute-forcing credentials, and executing unusual Windows Registry operations. Notably, Windows Registry events involving "Schedule\Taskcache\Tasks" contain subkeys for individual tasks, storing GUIDs that can be used to locate and analyze scheduled tasks. Additionally, Control\WMI\Security holds security descriptors for WMI providers and Event Tracing loggers that use non-default security settings respectively.

Furthermore, Darktrace identified data exfiltration activity over SSH, including connections to an ASN associated with a malicious hosting service geolocated in Russia.

1. Network Scan & Brute Force

Darktrace identified anomalous behavior in late August to early September 2025, originating from a source device engaging in internal network scanning followed by brute-force attempts targeting administrator credential, including “administrator”, “Admin”, “rdpadmin”, “ftpadmin”.

Upon further analysis, one of the HTTP connections seen in this activity revealed the use of the user agent string “OpenVAS-VT”, suggesting that the device was using the OpenVAS vulnerability scanner. Subsequently, additional devices began exhibiting network scanning behavior. During this phase, a file named “delete.me” was deleted by multiple devices using SMB protocol. This file is commonly associated with network scanning and penetration testing tool NetScan.

2. Windows Registry Key Update

Following the scanning phase, Darktrace observed the initial device then performing suspicious Winreg operations. This included the use of the ”BaseRegOpenKey” function across multiple registry paths.

Additional operations such as “BaseRegOpenKey” and “BaseRegQueryValue” were also seen around this time. These operations are typically used to retrieve specific registry key values and allow write operations to registry keys.

The registry keys observed included “SYSTEM\CurrentControlSet\Control\WMI\Security” and “Software\Microsoft\Windows NT\CurrentVersion\Schedule\Taskcache\Tasks”. These keys can be leveraged by malicious actors to update WMI access controls and schedule malicious tasks, respectively, both of which are common techniques for establishing persistence within a compromised system.

3. New Administrator Credential Usage

Darktrace subsequently detected the device using a highly privileged credential, “administrator”, via a successful Kerberos login for the first time. Shortly after, the same credential was used again for a successful SMB session.

These marked the first instances of authentication using the “administrator” credential across the customer’s environment, suggesting potential malicious use of the credential following the earlier brute-force activity.

Figure 1: Darktrace’s detection of administrator credentials being used in Kerberos login events by an infected device.

Figure 2: Darktrace’s detection of administrator credentials being used in SMB sessions by an infected device.

4. Data Exfiltration

Prior to ransomware deployment, several infected devices were observed exfiltrating data to the malicious IP 45.135.232[.]229 via SSH connections [7][8]. This was followed by the device downloading data from other internal devices and transferring an unusually large volume of data to the same external endpoint.

The IP address was first seen on the network on September 2, 2025 - the same date as the observed data exfiltration activity preceding ransomware deployment and encryption.

Further analysis revealed that the endpoint was geolocated in Russia and registered to the malicious hosting provider Proton66. Multiple external researchers have reported malicious activity involving the same Proton66 ASN (AS198953 Proton66 OOO) as far back as April 2025. These activities notably included vulnerability scanning, exploitation attempts, and phishing campaigns, which ultimately led to malware [4][5][6].

Data Exfiltration Endpoint details.

Endpoint: 45.135.232[.]229
ASN: AS198953 Proton66 OOO
Transport protocol: TCP
Application protocol: SSH
Destination port: 22

Figure 3: Darktrace’s summary of the external IP 45.135.232[.]229, first detected on September 2, 2025. The right-hand side showcases model alerts triggered related to this endpoint including multiple data exfiltration related model alerts.

Further investigation into the endpoint using open-source intelligence (OSINT) revealed that it led to a Microsoft Internet Information Services (IIS) Manager console webpage. This interface is typically used to configure and manage web servers. However, threat actors have been known to exploit similar setups, using fake certificate warnings to trick users into downloading malware, or deploying malicious IIS modules to steal credentials.

Figure 4: Live screenshot of the destination (45.135.232[.]229), captured via OSINT sources, displaying a Microsoft IIS Manager console webpage.

5. Ransomware Encryption & Ransom Note

Multiple devices were later observed connecting to internal devices via SMB and performing a range of actions indicative of file encryption. This suspicious activity prompted Darktrace’s Cyber AI Analyst to launch an autonomous investigation, during which it pieced together associated activity and provided concrete timestamps of events for the customer’s visibility.

During this activity, several devices were seen writing a file named “readme.txt” to multiple locations, including network-accessible webroot paths such as inetpub\ and wwwroot\. This “readme.txt” file, later confirmed to be the ransom note, claimed the threat actors were affiliated with DragonForce.

At the same time, devices were seen performing SMB Move, Write and ReadWrite actions involving files with the “.df_win” extension across other internal devices, suggesting that file encryption was actively occurring.

Figure 5: Darktrace’s detection of SMB events (excluding Read events) where the device was seen moving or writing files with the “.df_win” extension.

Figure 6: Darktrace’s detection of a spike in SMB Write events with the filename “readme.txt” on September 9, indicating the start of file encryption.

Conclusion

The rise of Ransomware-as-a-Service (RaaS) and increased attacker customization is fragmenting tactics, techniques, and procedures (TTPs), making it increasingly difficult for security teams to prepare for and defend against each unique intrusion. RaaS providers like DragonForce further complicate this challenge by enabling a wide range of affiliates, each with varying levels of sophistication [9].

In this instance, Darktrace was able to identify several stages of the attack kill chain, including network scanning, the first-time use of privileged credentials, data exfiltration, and ultimately ransomware encryption. Had the customer enabled Darktrace’s Autonomous Response capability, it would have taken timely action to interrupt the attack in its early stages, preventing the eventual data exfiltration and ransomware detonation.

Credit to Justin Torres, Senior Cyber Analyst, Nathaniel Jones, VP, Security & AI Strategy, FCISO, & Emma Foulger, Global Threat Research Operations Lead.

Edited by Ryan Traill (Analyst Content Lead)

[related-resource]

Appendices

References:

1. https://www.infosecurity-magazine.com/news/dragonforce-goup-ms-coop-harrods/

2. https://www.picussecurity.com/resource/blog/dragonforce-ransomware-attacks-retail-giants

3. https://blog.checkpoint.com/security/dragonforce-ransomware-redefining-hybrid-extortion-in-2025/

4. https://www.trustwave.com/en-us/resources/blogs/spiderlabs-blog/proton66-part-1-mass-scanning-and-exploit-campaigns/

5. https://www.trustwave.com/en-us/resources/blogs/spiderlabs-blog/proton66-part-2-compromised-wordpress-pages-and-malware-campaigns/

6. https://www.broadcom.com/support/security-center/protection-bulletin/proton66-infrastructure-tied-to-expanding-malware-campaigns-and-c2-operations

7. https://www.virustotal.com/gui/ip-address/45.135.232.229

8. https://spur.us/context/45.135.232.229

9. https://www.group-ib.com/blog/dragonforce-ransomware/

IoC - Type - Description + Confidence

· 45.135.232[.]229 - Endpoint Associated with Data Exfiltration

· .readme.txt – Ransom Note File Extension

· .df_win – File Encryption Extension Observed

MITRE ATT&CK Mapping

DragonForce TTPs vs Darktrace Models

Initial Access:

· Anomalous Connection::Callback on Web Facing Device

Command and Control:

· Compromise::SSL or HTTP Beacon

· Compromise::Beacon to Young Endpoint

· Compromise::Beaconing on Uncommon Port

· Compromise::Suspicious SSL Activity

· Anomalous Connection::Devices Beaconing to New Rare IP

· Compromise::Suspicious HTTP and Anomalous Activity

· DNS Tunnel with TXT Records

Tooling:

· Anomalous File::EXE from Rare External Location

· Anomalous File::Masqueraded File Transfer

· Anomalous File::Numeric File Download

· Anomalous File::Script from Rare External Location

· Anomalous File::Uncommon Microsoft File then Exe

· Anomalous File::Zip or Gzip from Rare External Location

· Anomalous File::Uncommon Microsoft File then Exe

· Anomalous File::Internet Facing System File Download

Reconnaissance:

· Device::Suspicious SMB Query

· Device::ICMP Address Scan

· Anomalous Connection::SMB Enumeration

· Device::Possible SMB/NTLM Reconnaissance

· Anomalous Connection::Possible Share Enumeration Activity

· Device::Possible Active Directory Enumeration

· Anomalous Connection::Large Volume of LDAP Download

· Device::Suspicious LDAP Search Operation

Lateral Movement:

· User::Suspicious Admin SMB Session

· Anomalous Connection::Unusual Internal Remote Desktop

· Anomalous Connection::Unusual Long Remote Desktop Session

· Anomalous Connection::Unusual Admin RDP Session

· User::New Admin Credentials on Client

· User::New Admin Credentials on Server

· Multiple Device Correlations::Spreading New Admin Credentials

· Anomalous Connection::Powershell to Rare External

· Device::New PowerShell User Agent

· Anomalous Active Directory Web Services

· Compromise::Unusual SVCCTL Activity

Evasion:

· Unusual Activity::Anomalous SMB Delete Volume

· Persistence

· Device::Anomalous ITaskScheduler Activity

· Device::AT Service Scheduled Task

· Actions on Objectives

· Compromise::Ransomware::Suspicious SMB Activity (EM)

· Anomalous Connection::Sustained MIME Type Conversion

· Compromise::Ransomware::SMB Reads then Writes with Additional Extensions

· Compromise::Ransomware::Possible Ransom Note Write

· Data Sent to Rare Domain

· Uncommon 1 GiB Outbound

· Enhanced Unusual External Data Transfer

Darktrace Cyber AI Analyst Coverage/Investigation Events:

· Web Application Vulnerability Scanning of Multiple Devices

· Port Scanning

· Large Volume of SMB Login Failures

· Unusual RDP Connections

· Widespread Web Application Vulnerability Scanning

· Unusual SSH Connections

· Unusual Repeated Connections

· Possible Application Layer Reconnaissance Activity

· Unusual Administrative Connections

· Suspicious Remote WMI Activity

· Extensive Unusual Administrative Connections

· Suspicious Directory Replication Service Activity

· Scanning of Multiple Devices

· Unusual External Data Transfer

· SMB Write of Suspicious File

· Suspicious Remote Service Control Activity

· Access of Probable Unencrypted Password Files

· Internal Download and External Upload

· Possible Encryption of Files over SMB

· SMB Writes of Suspicious Files to Multiple Devices

_{The content provided in this blog is published by Darktrace for general informational purposes only and reflects our understanding of cybersecurity topics, trends, incidents, and developments at the time of publication. While we strive to ensure accuracy and relevance, the information is provided “as is” without any representations or warranties, express or implied. Darktrace makes no guarantees regarding the completeness, accuracy, reliability, or timeliness of any information presented and expressly disclaims all warranties.}

_{Nothing in this blog constitutes legal, technical, or professional advice, and readers should consult qualified professionals before acting on any information contained herein. Any references to third-party organizations, technologies, threat actors, or incidents are for informational purposes only and do not imply affiliation, endorsement, or recommendation.}

_{Darktrace, its affiliates, employees, or agents shall not be held liable for any loss, damage, or harm arising from the use of or reliance on the information in this blog.}

_{The cybersecurity landscape evolves rapidly, and blog content may become outdated or superseded. We reserve the right to update, modify, or remove any content.}

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July 8, 2026

Securing AI: Analysis of the Complete Security Stack with Governance and Controls

Why traditional cybersecurity approaches are not enough for AI

AI adoption outpaces most security programs’ ability to adapt. That gap is now one of the most consequential sources of cyber risk facing enterprises. As organizations embed generative and agentic AI into development workflows, business operations, and security tooling itself, the question is no longer whether AI will introduce risk. The question is whether organizations understand where that risk actually lives and how to manage it operationally.

Two recent pieces of guidance underscore this shift:

The upcoming Cybersecurity Framework Profile for AI from NIST

The Five Eyes government guidance on the careful adoption of agentic AI services

Taken together, they point to a critical conclusion. AI security cannot be reduced to model hardening or prompt filtering. It requires a defense in depth strategy that treats AI as both a new attack surface and a force multiplier for defense, while accounting for how AI fundamentally changes scale, speed, and autonomy.

Recent threat research suggests that today's cyber risk is driven less by initial compromise and more by an adversary's ability to blend into normal operations over time. AI systems create the same exposure in a new form: more autonomy, more scale, and more opportunities for risky behavior to blend into normal operations.

How NIST defines the three core pillars of AI security

The NIST profile organizes AI risk across three inseparable focus areas that span all cybersecurity functions, Secure, Defend and Thwart. These areas are not sequential. They exist simultaneously and must be addressed together.

Secure

This treats AI as an attack surface. It includes models, prompts, agents, pipelines, training and inference data, retrieval augmented generation corpora, and the AI supply chain itself. AI systems are opaque, probabilistic, and non-deterministic by design. Some vulnerabilities are inherent in how models are trained or how data is sourced. Traditional patching does not fully mitigate these risks. This is also where many enterprises are weakest today and, critically, where many security programs stop.

Defend

This is AI as a defensive force multiplier. AI can improve detection speed, scale, correlation, and response, but only if the right models are used and operationalized correctly. Machine-speed behavior-based detection, response and containment becomes critical in defending non-deterministic systems. Accuracy, explainability, governance, testing, validation, and integration into SOC workflows matter as much as capability. Without those controls, hallucination risk, over automation, and misplaced trust become security risks themselves.

Thwart

This treats AI as an adversarial accelerant. Threat actors are already using AI to generate targeted social engineering attacks, deepfakes, malware, and autonomous attack agents. Asymmetric warfare is highlighting faster vulnerability discovery and exploitation with a lag on patch development, testing and deployment.

How this looks in practice

Darktrace researchers observed scaled, automated exploitation of the React2Shell vulnerability within days of disclosure. A vulnerable cloud asset was exploited in under 120 seconds of being deployed. Darktrace research team observed an AI/LLM-generated malware sample used in exploitation activity tied to React2Shell. The significance isn't novelty. It is that AI lowers the barrier to producing usable offensive tooling and compresses the time between experimentation and deployment.

Tactics are getting more and more creative in order to string together steps of an attack kill chain. This creates a dependency on behavior-based detection, autonomous investigation, autonomous containment, training, resilience investment, and recovery planning across the entire enterprise.

Why agentic AI fundamentally changes enterprise cyber risk

The Five Eyes guidance on agentic AI highlights material changes to the cyber risk profile of an organization. Unlike generative AI systems that produce content for human consumption, agentic AI systems reason, plan, and act autonomously across tools, data, and environments. That autonomy, combined with access to real systems, amplifies the impact of traditional cyber failures and introduces new system level risks that are difficult to predict, observe, and contain.

Risk in agentic systems does not live in the model alone. It emerges from interactions between models, prompts, memory, tools, APIs, identities, privileges, inter-agent trust relationships, and human assumptions baked into design. Vulnerabilities are often introduced through data, connectors, natural language interfaces, protocols, and drift by design.

In supply-chain incidents, attackers did not need sophisticated exploits to scale impact. They abused trusted systems built for automation and implicit access. Agentic AI inherits that model. Once a system can act across tools, data, and workflows, compromise propagates through trust relationships that were never designed for machine autonomy.

The major agentic AI risk classes include the following:

The identity control for non-human identities or autonomous agents makes it difficult to mitigate over-permissioning, limiting access, scope, and duration, as well as access hygiene

Agents are frequently over permissioned

Compromised tools inherit agent authority

Static secrets enable impersonation

Implicit trust between agents enables lateral movement

Design and configuration risks compound this, including privileges evaluated once at startup, poor segmentation, unvetted third party tools, reused authorization decisions outside their original context, and guardrail limitations.

Behavioral risk

Agents can optimize for goals in unsafe ways, misinterpret ambiguous intent, chain actions into unintended sequences, change behavior during evaluation, and exhibit deceptive or sycophantic responses.

Structural risk

Structural risk follows from agentic systems that are tightly coupled, multicomponent ecosystems. Failures can propagate across agents. Hallucinations cascade downstream. Resource exhaustion becomes systemic. Tool misuse enables indirect prompt injection and command execution. Rogue agents can poison peer agents through trust relationships.

Accountability

Accountability becomes unclear as autonomy increases. Autonomous agents assume human identity permissions, and humans should have clear ownership of these agents, but they don’t, and this model is flawed. Decision paths are opaque and non-deterministic. Logs are fragmented and difficult to interpret. Reproducing an incident will be impossible without explicit design for observability and forensics. An agent compromise is functionally an insider threat, often with better access and fewer behavioral constraints than a human.

What does defense in depth look like for AI?

Agentic AI runs on software, networks, identities, and data. It must be governed using the same foundational principles that have proven resilient under uncertainty, including secure by design, defense in depth, zero trust, least privilege, continuous monitoring, behavior-based advanced threat detection and containment, and incident response and recovery.

Core components to a Defense in depth Strategy for Securing the use of AI:

Strong, precise identity control plane to include an identity per agent (cryptographic, non‑shared)
- Privilege monitoring and just‑in‑time access

Data Governance

Secure‑by‑default configurations
- Security Posture Management
- Zero Trust principles

Strong guardrails, deny‑by‑default policies, and isolation

Explicit instruction hierarchies and controlled context

Behavioral-based detection across entire enterprise to include inputs, tools, and outputs as well as AI used on the endpoint, across the network, cloud, SaaS, email, and OT
- Runtime anomaly detection and goal‑drift detection
- Autonomous containment to mitigate risk and minimize damage

Hard boundaries on autonomy and delegation

Testing, Evaluation, Validation and Verification
- Determine when autonomous action and when human in the loop
- Adversarial training and agent‑specific testing
- Simulation, red teaming, and chaos testing

Kill‑switches, rollback, and containment mechanisms
- Forensics data captures, interpretability, autonomous containment, and remediation/recovery plans

Until standards, tooling, and assurance methods mature, organizations should assume agentic AI systems will behave unexpectedly and design deployments around resilience, behavior-based detection, reversibility, and containment, not efficiency.

How security leaders should prepare for enterprise AI adoption

AI security is not model security alone. Data, pipelines, identities, and agents are first class assets. Many AI attacks succeed through standard cyber failures amplified by AI. Identity, data, and supply chain risk dominate. Behavior-based detection and response are critical, not optional. Logging, provenance, versioning, and forensics data capture of detections are mandatory because you cannot investigate or recover from AI incidents without them.

Risk will often be visible in behavior before it is clearly defined in policy or guidance. The same pattern has been seen in pre-CVE disclosure detection, where abnormal activity appears before the industry has named or described the vulnerability. AI systems introduce that uncertainty by design.

Security leaders should prioritize controls before AI is fully deployed, avoid generic AI security checklists, integrate AI risk into existing cyber programs, and mitigate the risk of non-deterministic technology with continuous oversight, monitoring, behavior analytics, anomaly detection, autonomous investigation, and autonomous containment.

Visibility has a different connotation with AI. Previously, audit logging worked for software/people, but with Generative AI-based systems, interpretability and explainability is difficult to understand, you cannot "undo" what has been done, or see the logic or control a chain of events. This is why behavioral-based detections and containment becomes critical.

What capabilities should every AI security program include?

If an organization asked “what must be in place before scaling AI?”:

AI Risk board and approval workflow

IAM + PAM for all AI services and agents

AI asset inventory

Prompt/output DLP with sanctioned AI access – This is not just pre- and post- filters, but behavior-based detections of semantic interface as well as behavior-based analysis of output with associated risk context.

Shadow AI identification

Secure MLOps – This is an entire paper itself

Runtime guardrails and tool restrictions
- Including AI Gateway/SASE/Zero trust/

Runtime security with behavior-based detections
- Complete visibility, monitoring, behavior analytics, anomaly detection, risk/intent/context evaluation of anomalies, autonomous investigation and autonomous containment of all AI assets across endpoint, network, SaaS, SASE, cloud, OT, email, and messaging platforms

Secure data pipelines and data governance

SOC workflow changes from malicious classification workflows to behavior-based detection workflows

Remediation plans for AI-related incidents

Layered Governance and Security Stack for Securing AI

The following outline considers governance and security tools that should be considered, well-integrated, deployed, tested, operationalized and embedded within security workflows. These tools and controls map to NIST’s CMF for AI.

These considerations do not need to be implemented in order. Runtime Detect and Respond will help mitigate risk while Governance, Visibility, and Identity mature.

Category	Tooling	Controls
Governance & Visibility	AI asset inventory / AI CMDB Shadow AI discovery SaaS discovery AI usage on non-endpoint managed systems via network or cloud telemetry MCP server/client usage via protocols Browser telemetry Gateway or SASE telemetry	Establish a risk board to set up controls Mandatory registration of AI systems Owner, data classification, intended use, and risk tier Supplier disclosure requirements Risk mitigation plan for AI adoption, innovation, or development
Identity, Access & Agent Control	Non-human autonomous agents should not have the full permissions associated with a human user. IAM with workload identities PAM for AI service accounts Secrets management with short-lived tokens Zero Trust principles Identity, permission, and token hygiene	Unique identities per model, agent, and pipeline Least privilege for tools, data, and APIs Explicit approval for autonomous actions
Data Security & Privacy	Data classification and labeling Enterprise DLP across endpoint, email, network, cloud, and SaaS Forensics data capture after risky detections	Prompt-level DLP through behavior-based semantic analysis with risk and intent context Input/interface analysis for risky data requests Output analysis for sensitive data Data integrity evaluation Retention and redaction policies for prompts and responses
Secure MLOps / LLMOps	Secure CI/CD with AI-specific gates Model registries with approval workflows Dependency, container, and artifact scanning SBOM/AIBOM generation IaC security scanning Security posture management Misconfiguration identification Hardening recommendations	Signed models and prompts Versioned datasets, configurations, logging, and controls Securing data pipelines Controlled promotion Quality assurance Adversarial testing
Runtime Security	Securing runtime goes beyond guardrails and model firewalls to include behavior-based detections, response, and containment. Detection, monitoring, and SOC integration Centralized visibility into prompts, outputs, and tool calls AI-specific detections Behavior-based detection for AI usage patterns Model drift and behavior monitoring Autonomous containment Behavior-based detection of model inputs and outputs Prompt injection detection Model manipulation, including jailbreaking, poisoning, and related attacks Sensitive data access attempts Behavior-based detection across low-code agents, high-code agents, MCP clients and servers, endpoint, network, cloud, email, SaaS, SASE, IoT, and OT	Policy enforcement between users, models, tools, agents, SaaS models/tools, and MCP servers/clients Risk, intent, and context evaluation for detections and response actions
Response & Recovery	Autonomous containment AI-assisted playbooks Forensics data capture for AI-related events Model rollback mechanisms Backup and restore for models and datasets	Kill switch for agents Autonomous response to agents performing risky behaviors Model and dataset rollback Remediation plans Tabletop exercises Supplier coordination plans Post-incident AI performance validation

AI security requires continuous visibility and behavioral detection

AI changes how fast systems move, how decisions are made, and how risk propagates. It does not change the fundamentals of security. Organizations that succeed will be the ones that apply those fundamentals rigorously, assume failure, and build systems that can detect, contain, and recover when AI behaves in ways they did not anticipate. Security is not what AI is allowed to do. It is whether the organization can understand, trust, and control what AI actually does in practice.

Take this guidance to understand different initiatives that organizations should be considering. Securing AI is the most critical component to AI safety. As organizations invest more in AI adoption, they should be investing in security in order to mitigate the risk of AI adoption. Organizations should be evaluating their governance and security stack to include well-integrated tools that are deployed, tested, operationalized and embedded within security workflows. While organizations mature in governance, visibility and identity access management, they should be investing in behavior-based detection and autonomous containment to mitigate AI risk.

About the author

Blog

July 6, 2026

NIST Just Proved It: AI Security Can’t Be Solved With Rules

Static AI guardrails are inherently limited

As organizations adopt generative AI, many still assume that the right set of guardrails will be enough. The problem is you can’t anticipate every way these systems might be misused, abused or attacked. What NIST has done is put a mathematical foundation under that intuition.

In recent research building on Gödel’s incompleteness theorems, which showed that any system built on a fixed set of rules will always have gaps, NIST demonstrates that there is no finite set of guardrails that can be universally robust against adversarial prompts. In plain terms, if your defense is based on a fixed set of rules, there will always be inputs that bypass them. Not because the rules are badly written, but because the problem space is bigger than static rules can ever cover.

This is not new in cybersecurity - detection rules have always had to live with this trade-off. What is different with GenAI is the scale and shape of that problem. These systems are built on human language, and human language is not bounded. It is fluid, contextual and deliberately ambiguous. The number of ways intent can be hidden is effectively limitless. You are not defending against a defined protocol or a fixed exploit chain. You are defending against the entire expressive capacity of people.

So attempting to create a complete set of rules is the wrong starting point. It assumes the problem can be deterministically described. NIST’s work shows that it cannot. Organizations still need a way to manage AI risk, but the traditional approach of defining allowed and disallowed patterns is always going to lag behind what is actually happening. The same input can be benign in one context and risky in another, and static rules struggle to capture that distinction.

The question then is what fills that gap?

AI security must shift from rules to behavior

What's required is a shift in what you are trying to understand. Rules try to describe what should and shouldn't happen. Behavior shows you what is happening. Or to put it another way, if inputs are unbounded and adversaries adapt, the only stable signal is behavior.

In a GenAI context, that means analyzing how an AI model is being used, how prompts evolve over time, how outputs are shaped, and where AI agent interactions start to drift from what is expected. It means moving from static definitions of bad to a more dynamic understanding of intent.

Instead of trying to predict every bad prompt, you focus on identifying when behavior starts to move outside expected norms. Instead of asking whether a single input matches a rule, you ask whether the overall pattern of activity makes sense for the system and how it’s being used.

Guardrails remain important but they are only one layer

This does not eliminate the need for guardrails. They still play a role. But they will never address the entire problem space and are simply one part of your defense in depth approach.

NIST’s proof is useful because it makes this explicit. It removes the assumption that with enough effort, a complete rule set is achievable. It isn’t.

Once you accept that, the shift becomes unavoidable. This is no longer a problem of writing better rules, but of understanding behavior in a space where the possible inputs are effectively unbounded.

For security leaders, that changes the nature of the problem. It is less about defining what should be allowed, and more about recognizing when something is no longer consistent with expected behavior.

That does not remove the need for guardrails, but it does change their role. They set boundaries, but they do not define understanding. The gap between the two is where risk now sits.

In the end, this is what “can’t be solved with rules” really means. Rules will always leave gaps, and those gaps are not theoretical. They show up in how systems actually behave Not what we expect them to do, or what we intended them to do, but what they are doing in practice. That is where the signal is, and increasingly, that is where the security problem sits.

References:

https://www.nist.gov/news-events/news/2026/06/nist-mathematical-proof-supports-transition-continuous-monitor-and-update

https://ieeexplore.ieee.org/document/11475847

About the author

Andrew Hollister

Principal Solutions Engineer, Cyber Technician

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Check out this article by Darktrace: Tracking a Dragon: Investigating a DragonForce-affiliated ransomware attack with Darktrace

Tracking a Dragon: Investigating a DragonForce-affiliated ransomware attack with Darktrace

What is DragonForce?

Darktrace's Observations

1. Network Scan & Brute Force

2. Windows Registry Key Update

3. New Administrator Credential Usage

4. Data Exfiltration

5. Ransomware Encryption & Ransom Note

Conclusion

Appendices

References:

IoC - Type - Description + Confidence

MITRE ATT&CK Mapping

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Why traditional cybersecurity approaches are not enough for AI

How NIST defines the three core pillars of AI security

Secure

Defend

Thwart

How this looks in practice

Why agentic AI fundamentally changes enterprise cyber risk

Behavioral risk

Structural risk

Accountability

What does defense in depth look like for AI?

How security leaders should prepare for enterprise AI adoption

What capabilities should every AI security program include?

Layered Governance and Security Stack for Securing AI

AI security requires continuous visibility and behavioral detection

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July 6, 2026

NIST Just Proved It: AI Security Can’t Be Solved With Rules

Static AI guardrails are inherently limited

AI security must shift from rules to behavior

Guardrails remain important but they are only one layer