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July 4, 2024

A Busy Agenda: Darktrace's Detection of Qilin Ransomware as a Service Operator

This blog breaks down how Darktrace detected and analyzed Qilin, a Ransomware-as-a-Service group behind recent high-impact attacks. You’ll see how Qilin affiliates customize attacks with flexible encryption, process termination, and double-extortion techniques, as well as why its cross-platform builds in Rust and Golang make it especially evasive. Darktrace highlights three real-world cases where its AI identified likely Qilin activity across customer environments, offering insights into how behavioral detection can spot novel ransomware before disruption occurs. Readers will gain a clear view of Qilin’s toolkit, tactics, and how self-learning defense adapts to these evolving threats.
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
Alexandra Sentenac
Cyber Analyst
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04
Jul 2024

Qilin ransomware has recently dominated discussions across the cyber security landscape following its deployment in an attack on Synnovis, a UK-based medical laboratory company. The ransomware attack ultimately affected patient services at multiple National Health Service (NHS) hospitals that rely on Synnovis diagnostic and pathology services. Qilin’s origins, however, date back further to October 2022 when the group was observed seemingly posting leaked data from its first known victim on its Dedicated Leak Site (DLS) under the name Agenda[1].

The Darktrace Threat Research team investigated network artifacts related to Qilin and identified three probable cases of the ransomware across the Darktrace customer base between June 2022 and May 2024.

Qilin Ransomware-as-a-Service Operator

Qilin operates as a Ransomware-as-a-Service (RaaS) that employs double extortion tactics, whereby harvested data is exfiltrated and threatened of publication on the group's DLS, which is hosted on Tor. Qilin ransomware has samples written in both the Golang and Rust programming languages, making it compilable with various operating systems, and is highly customizable. When building Qilin ransomware variants to be used on their target(s), affiliates can configure settings such as the encryption mode (i.e., skip-step, percent, and speed), the file extension being appended, files, extensions and directories to be skipped during the encryption, and the processes and services to be terminated, among others[1] [2].  

Trend Micro analysts, who were the first to discover Qilin samples in August 2022, when the name "Agenda" was still used in ransom notes, found that each analyzed sample was customized for the intended victims and that "unique company IDs were used as extensions of encrypted files" [3]. This information is configurable from within the Qilin's affiliate panel's 'Targets' section, shown below. The panel's background image features the eponym Chinese legendary chimerical creature Qilin (pronounced “Ke Lin”). Despite this Chinese mythology reference, Russian language was observed being used by a Qilin operator in an underground forum post aimed at hiring affiliates and advertising their RaaS operation[2].

Figure 1: Qilin ransomware’s affiliate panel.

Qilin's RaaS program purportedly has an attractive affiliates' payment structure, with affiliates allegedly able to earn 80% of ransom payments of USD 3m or less and 85% for payments above that figure[2], making it a possibly appealing option in the RaaS ecosystem.  Publication of stolen data and ransom payment negotiations are purportedly handled by Qilin operators. Qilin affiliates have been known to target companies located around the world and within a variety of industries, including critical sectors such as healthcare and energy.

As Qilin is a RaaS operation, the choice of targets does not necessarily reflect Qilin operators' intentions, but rather that of its affiliates.  Similarly, the tactics, techniques, procedures (TTPs) and indicators of compromise (IoC) identified by Darktrace are associated with the given affiliate deploying Qilin ransomware for their own purpose, rather than TTPs and IoCs of the Qilin group. Likewise, initial vectors of infection may vary from affiliate to affiliate. Previous studies show that initial access to networks were gained via spear phishing emails or by leveraging exposed applications and interfaces.

Differences have been observed in terms of data exfiltration and potential C2 external endpoints, suggesting the below investigations are not all related to the same group or actor(s).

Darktrace’s Threat Research Investigation

June 2022

Darktrace first detected an instance of Qilin ransomware back in June 2022, when an attacker was observed successfully accessing a customer’s Virtual Private Network (VPN) and compromising an administrative account, before using RDP to gain access to the customer’s Microsoft System Center Configuration Manager (SCCM) server

From there, an attack against the customer's VMware ESXi hosts was launched. Fortunately, a reboot of their virtual machines (VM) caught the attention of the security team who further uncovered that custom profiles had been created and remote scripts executed to change root passwords on their VM hosts. Three accounts were found to have been compromised and three systems encrypted by ransomware.  

Unfortunately, Darktrace was not configured to monitor the affected subnets at the time of the attack. Despite this, the customer was able to work directly with Darktrace analysts via the Ask the Expert (ATE) service to add the subnets in question to Darktrace’s visibility, allowing it to monitor for any further unusual behavior.

Once visibility over the compromised SCCM server was established, Darktrace observed a series of unusual network scanning activities and the use of Kali (a Linux distribution designed for digital forensics and penetration testing). Furthermore, the server was observed making connections to multiple rare external hosts, many using the “[.]ru” Top Level Domain (TLD). One of the external destinations the server was attempting to connect was found to be related to SystemBC, a malware that turns infected hosts into SOCKS5 proxy bots and provides command-and-control (C2) functionality.

Additionally, the server was observed making external connections over ports 993 and 143 (typically associated with the use of the Interactive Message Access Protocol (IMAP) to multiple rare external endpoints. This was likely due to the presence of Tofsee malware on the device.

After the compromise had been contained, Darktrace identified several ransom notes following the naming convention “README-RECOVER-<extension/company_id>.txt”” on the network. This naming convention, as well as the similar “<company_id>-RECOVER-README.txt” have been referenced by open-source intelligence (OSINT) providers as associated with Qilin ransom notes[5] [6] [7].

April 2023

The next case of Qilin ransomware observed by Darktrace took place in April 2023 on the network of a customer in the manufacturing sector in APAC. Unfortunately for the customer in this instance, Darktrace RESPOND™ was not active on their environment and no autonomous response actions were taken to contain the compromise.

Over the course of two days, Darktrace identified a wide range of malicious activity ranging from extensive initial scanning and lateral movement attempts to the writing of ransom notes that followed the aforementioned naming convention (i.e., “README-RECOVER-<extension/company_id>.txt”).

Darktrace observed two affected devices attempting to move laterally through the SMB, DCE-RPC and RDP network protocols. Default credentials (e.g., UserName, admin, administrator) were also observed in the large volumes of SMB sessions initiated by these devices. One of the target devices of these SMB connections was a domain controller, which was subsequently seen making suspicious WMI requests to multiple devices over DCE-RPC and enumerating SMB shares by binding to the ‘server service’ (srvsvc) named pipe to a high number of internal devices within a short time frame. The domain controller was further detected establishing an anomalously high number of connections to several internal devices, notably using the RDP administrative protocol via a default admin cookie.  

Repeated connections over the HTTP and SSL protocol to multiple newly observed IPs located in the 184.168.123.0/24 range were observed, indicating C2 connectivity.  WebDAV user agent and a JA3 fingerprint potentially associated with Cobalt Strike were notably observed in these connections. A few hours later, Darktrace detected additional suspicious external connections, this time to IPs associated with the MEGA cloud storage solution. Storage solutions such as MEGA are often abused by attackers to host stolen data post exfiltration. In this case, the endpoints were all rare for the network, suggesting this solution was not commonly used by legitimate users. Around 30 GB of data was exfiltrated over the SSL protocol.

Darktrace did not observe any encryption-related activity on this customer’s network, suggesting that encryption may have taken place locally or within network segments not monitored by Darktrace.

May 2024

The most recent instance of Qilin observed by Darktrace took place in May 2024 and involved a customer in the US. In this case, Darktrace initially detected affected devices using unusual administrative and default credentials, before additional internal systems were observed making extensive suspicious DCE-RPC requests to a range of internal locations, performing network scanning, making unusual internal RDP connections, and transferring suspicious executable files like 'a157496.exe' and '83b87b2.exe'.  SMB writes of the file "LSM_API_service" were also observed, activity which was considered 100% unusual by Darktrace; this is an RPC service that can be abused to enumerate logged-in users and steal their tokens. Various repeated connections likely representative of C2 communications were detected via both HTTP and SSL to rare external endpoints linked in OSINT to Cobalt Strike use. During these connections, HTTP GET requests for the following URIs were observed:

/asdffHTTPS

/asdfgdf

/asdfgHTTP

/download/sihost64.dll

Notably, this included a GET request a DLL file named "sihost64.dll" from a domain controller using PowerShell.  

Over 102 GB of data may have been transferred to another previously unseen endpoint, 194.165.16[.]13, via the unencrypted File Transfer Protocol (FTP). Additionally, many non-FTP connections to the endpoint could be observed, over which more than 783 GB of data was exfiltrated. Regarding file encryption activity, a wide range of destination devices and shares were targeted.

Figure 2: Advanced Search graph displaying the total volume of data transferred over FTP to a malicious IP.

During investigations, Darktrace’s Threat Research team identified an additional customer, also based in the United States, where similar data exfiltration activity was observed in April 2024. Although no indications of ransomware encryption were detected on the network, multiple similarities were observed with the case discussed just prior. Notably, the same exfiltration IP and protocol (194.165.16[.]13 and FTP, respectively) were identified in both cases. Additional HTTP connectivity was further observed to another IP using a self-signed certificate (i.e., CN=ne[.]com,OU=key operations,O=1000,L=,ST=,C=KM) located within the same ASN (i.e., AS48721 Flyservers S.A.). Some of the URIs seen in the GET requests made to this endpoint were the same as identified in that same previous case.

Information regarding another device also making repeated connections to the same IP was described in the second event of the same Cyber AI Analyst incident. Following this C2 connectivity, network scanning was observed from a compromised domain controller, followed by additional reconnaissance and lateral movement over the DCE-RPC and SMB protocols. Darktrace again observed SMB writes of the file "LSM_API_service", as in the previous case, activity which was also considered 100% unusual for the network. These similarities suggest the same actor or affiliate may have been responsible for activity observed, even though no encryption was observed in the latter case.

Figure 3: First event of the Cyber AI Analyst investigation following the compromise activity.

According to researchers at Microsoft, some of the IoCs observed on both affected accounts are associated with Pistachio Tempest, a threat actor reportedly associated with ransomware distribution. The Microsoft threat actor naming convention uses the term "tempest" to reference criminal organizations with motivations of financial gain that are not associated with high confidence to a known non-nation state or commercial entity. While Pistachio Tempest’s TTPs have changed over time, their key elements still involve ransomware, exfiltration, and extortion. Once they've gained access to an environment, Pistachio Tempest typically utilizes additional tools to complement their use of Cobalt Strike; this includes the use of the SystemBC RAT and the SliverC2 framework, respectively. It has also been reported that Pistacho Tempest has experimented with various RaaS offerings, which recently included Qilin ransomware[4].

Conclusion

Qilin is a RaaS group that has gained notoriety recently due to high-profile attacks perpetrated by its affiliates. Despite this, the group likely includes affiliates and actors who were previously associated with other ransomware groups. These individuals bring their own modus operandi and utilize both known and novel TTPs and IoCs that differ from one attack to another.

Darktrace’s anomaly-based technology is inherently threat-agnostic, treating all RaaS variants equally regardless of the attackers’ tools and infrastructure. Deviations from a device’s ‘learned’ pattern of behavior during an attack enable Darktrace to detect and contain potentially disruptive ransomware attacks.

Credit to: Alexandra Sentenac, Emma Foulger, Justin Torres, Min Kim, Signe Zaharka for their contributions.

References

[1] https://www.sentinelone.com/anthology/agenda-qilin/  

[2] https://www.group-ib.com/blog/qilin-ransomware/

[3] https://www.trendmicro.com/en_us/research/22/h/new-golang-ransomware-agenda-customizes-attacks.html

[4] https://www.microsoft.com/en-us/security/security-insider/pistachio-tempest

[5] https://www.trendmicro.com/en_us/research/22/h/new-golang-ransomware-agenda-customizes-attacks.html

[6] https://www.bleepingcomputer.com/forums/t/790240/agenda-qilin-ransomware-id-random-10-char;-recover-readmetxt-support/

[7] https://github.com/threatlabz/ransomware_notes/tree/main/qilin

Darktrace Model Detections

Internal Reconnaissance

Device / Suspicious SMB Scanning Activity

Device / Network Scan

Device / RDP Scan

Device / ICMP Address Scan

Device / Suspicious Network Scan Activity

Anomalous Connection / SMB Enumeration

Device / New or Uncommon WMI Activity

Device / Attack and Recon Tools

Lateral Movement

Device / SMB Session Brute Force (Admin)

Device / Large Number of Model Breaches from Critical Network Device

Device / Multiple Lateral Movement Model Breaches

Anomalous Connection / Unusual Admin RDP Session

Device / SMB Lateral Movement

Compliance / SMB Drive Write

Anomalous Connection / New or Uncommon Service Control

Anomalous Connection / Anomalous DRSGetNCChanges Operation

Anomalous Server Activity / Domain Controller Initiated to Client

User / New Admin Credentials on Client

C2 Communication

Anomalous Server Activity / Outgoing from Server

Anomalous Connection / Multiple Connections to New External TCP Port

Anomalous Connection / Anomalous SSL without SNI to New External

Anomalous Connection / Rare External SSL Self-Signed

Device / Increased External Connectivity

Unusual Activity / Unusual External Activity

Compromise / New or Repeated to Unusual SSL Port

Anomalous Connection / Multiple Failed Connections to Rare Endpoint

Device / Suspicious Domain

Device / Increased External Connectivity

Compromise / Sustained SSL or HTTP Increase

Compromise / Botnet C2 Behaviour

Anomalous Connection / POST to PHP on New External Host

Anomalous Connection / Multiple HTTP POSTs to Rare Hostname

Anomalous File / EXE from Rare External Location

Exfiltration

Unusual Activity / Enhanced Unusual External Data Transfer

Anomalous Connection / Data Sent to Rare Domain

Unusual Activity / Unusual External Data Transfer

Anomalous Connection / Uncommon 1 GiB Outbound

Unusual Activity / Unusual External Data to New Endpoint

Compliance / FTP / Unusual Outbound FTP

File Encryption

Compromise / Ransomware / Suspicious SMB Activity

Anomalous Connection / Sustained MIME Type Conversion

Anomalous File / Internal / Additional Extension Appended to SMB File

Compromise / Ransomware / Possible Ransom Note Write

Compromise / Ransomware / Possible Ransom Note Read

Anomalous Connection / Suspicious Read Write Ratio

IoC List

IoC – Type – Description + Confidence

93.115.25[.]139 IP C2 Server, likely associated with SystemBC

194.165.16[.]13 IP Probable Exfiltration Server

91.238.181[.]230 IP C2 Server, likely associated with Cobalt Strike

ikea0[.]com Hostname C2 Server, likely associated with Cobalt Strike

lebondogicoin[.]com Hostname C2 Server, likely associated with Cobalt Strike

184.168.123[.]220 IP Possible C2 Infrastructure

184.168.123[.]219 IP Possible C2 Infrastructure

184.168.123[.]236 IP Possible C2 Infrastructure

184.168.123[.]241 IP Possible C2 Infrastructure

184.168.123[.]247 IP Possible C2 Infrastructure

184.168.123[.]251 IP Possible C2 Infrastructure

184.168.123[.]252 IP Possible C2 Infrastructure

184.168.123[.]229 IP Possible C2 Infrastructure

184.168.123[.]246 IP Possible C2 Infrastructure

184.168.123[.]230 IP Possible C2 Infrastructure

gfs440n010.userstorage.me ga.co[.]nz Hostname Possible Exfiltration Server. Not inherently malicious; associated with MEGA file storage.

gfs440n010.userstorage.me ga.co[.]nz Hostname Possible Exfiltration Server. Not inherently malicious; associated with MEGA file storage.

Get the latest insights on emerging cyber threats

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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
Alexandra Sentenac
Cyber Analyst

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July 7, 2026

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

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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:

  1. The upcoming Cybersecurity Framework Profile for AI from NIST
  1. 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?”:

  1. AI Risk board and approval workflow
  1. IAM + PAM for all AI services and agents
  1. AI asset inventory
  1. 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.  
  1. Shadow AI identification
  1. Secure MLOps – This is an entire paper itself
  1. Runtime guardrails and tool restrictions
    • Including AI Gateway/SASE/Zero trust/
  1. 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
  1. Secure data pipelines and data governance
  1. SOC workflow changes from malicious classification workflows to behavior-based detection workflows
  1. 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.  

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July 6, 2026

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

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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

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About the author
Andrew Hollister
Principal Solutions Engineer, Cyber Technician
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