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August 10, 2022

Cyber Tactics in the Russo-Ukrainian Conflict

The conflict between Russia and Ukraine has led to fears of a full-scale cyberwar. Learn the cyber attack tactics used, hacking groups involved, and more!
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
Rosa Jong
OSINT Analyst
Written by
Taisiia Garkava
Security Analyst
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10
Aug 2022

Introduction

Since the beginning of the Russian invasion of Ukraine in February 2022, cyber communities around the world have been witnessing what can be called a ‘renaissance of cyberwarfare' [1]. Rather than being financially motivated, threat actors are being guided by political convictions to defend allies or attack their enemies. This blog reviews some of the main threat actors involved in this conflict and their ongoing tactics, and advises on how organizations can best protect themselves. Darktrace’s preliminary assessments predicted that attacks would be observed globally with a focus on pro-Ukrainian nations such as North Atlantic Treaty Organization (NATO) members and that identified Advanced Persistent Threat (APT) groups would develop new and complex malware deployed through increasingly sophisticated attack vectors. This blog will show that many of these assessments had unexpected outcomes.

Context for Conflict 

Cyber confrontation between Russia and Ukraine dates back to 2013, when Viktor Yanukovych, (former President of Ukraine) rejected an EU trade pact in favour of an agreement with Russia. This sparked mass protests leading to his overthrow, and shortly after, Russian troops annexed Crimea and initiated the beginning of Russian-Ukrainian ground and cyber warfare. Since then, Russian threat actors have been periodically targeting Ukrainian infrastructure. One of the most notable examples of this, an attack against their national power grid in December 2015, resulted in power outages for approximately 255,000 people in Ukraine and was later attributed to the Russian hacking group Sandworm [2 & 3]. 

Another well-known attack in June 2017 overwhelmed the websites of hundreds of Ukrainian organizations using the infamous NotPetya malware. This attack is still considered the most damaging cyberattack in history, with more than €10 billion euros in financial damage [4]. In February 2022, countries witnessed the next stage of cyberwar against Ukraine with both new and familiar actors deploying various techniques to target their rival’s critical infrastructure. 

Tactic 1: Ransomware

Although some sources suggest US ransomware incidents and expectations of ransom may have declined during the conflict, ransomware still remained a significant tactic deployed globally across this period [5] [6] [7]. A Ukrainian hacking group, Network Battalion 65 (NB65), used ransomware to attack the Russian state-owned television and radio broadcasting network VGTRK. NB65 managed to steal 900,000 emails and 4000 files, and later demanded a ransom which they promised to donate to the Ukrainian army. This attack was unique because the group used the previously leaked source code of Conti, another infamous hacker group that had pledged its support to the Russian government earlier in the conflict. NB65 modified the leaked code to make unique ransomware for each of its targets [5]. 

Against expectations, Darktrace’s customer base appeared to deviate from these ransom trends. Analysts have seen relatively unsophisticated ransomware attacks during the conflict period, with limited evidence to suggest they were connected to any APT activity. Between November 2021 and June 2022, there were 51 confirmed ransomware compromises across the Darktrace customer base. This represents an increase of 43.16% compared to the same period the year before, accounting for relative customer growth. Whilst this suggests an overall growth in ransom cases, many of these confirmed incidents were unattributed and did not appear to be targeting any particular verticals or regions. While there was an increase in the energy sector, this could not be explicitly linked to the conflict. 

The Darktrace DETECT family has a variety of models related to ransomware visibility:

Darktrace Detections for T1486 (Data Encrypted for Impact):

- Compromise / Ransomware / Ransom or Offensive Words Written to SMB

- Compromise / Ransomware / Suspicious SMB Activity

- Anomalous Connection / Sustained MIME Type Conversion

- Unusual Activity / Sustained Anomalous SMB Activity

- Compromise / Ransomware / Suspicious SMB File Extension

- Unusual Activity / Anomalous SMB Read & Write

- Unusual Activity / Anomalous SMB Read & Write from New Device

- SaaS / Resource / SaaS Resources with Additional Extensions

- Compromise / Ransomware / Possible Ransom Note Read

- [If RESPOND is enabled] Antigena / Network / External Threat / Antigena Ransomware Block

Tactic 2: Wipers

One of the largest groups of executables seen during the conflict were wipers. On the eve of the invasion, Ukrainian organizations were targeted by a new wiper malware given the name “HermeticWiper”. Hermetic refers to the name of the Cyprian company “Hermetica Digital Ltd.” which was used by attackers to request a code signing certificate [6]. Such a digital certificate is used to verify the ownership of the code and that it has not been altered. The 24-year-old owner of Hermetica Digital says he had no idea that his company was abused to retrieve a code signing certificate [7]. 

HermeticWiper consists of three components: a worm, decoy ransomware and the wiper malware. The custom worm designed for HermeticWiper was used to spread the malware across the network of its infected machines. ESET researchers discovered that the decoy ransomware and the wiper were released at the same time [8]. The decoy ransomware was used to make it look like the machine was hit by ransomware, when in reality the wiper was already permanently wiping data from the machines. In the attack’s initial stage, it bypasses Windows security features designed to prevent overwriting boot records by installing a separate driver. After wiping data from the machine, HermeticWiper prevents that data from being re-fragmented and overwrites the files to fragment it further. This is done to make it more challenging to reconstruct data for post-compromise forensics [9]. Overall, the function and purpose of HermeticWiper seems similar to that of NotPetya ransomware. 

HermeticWiper is not the only conflict-associated wiper malware which has been observed. In January 2022, Microsoft warned Ukrainian customers that they detected wiper intrusion activity against several European organizations. One example of this was the MBR (Master Boot Record) wiper. This type of wiper overwrites the MBR, the disk sector that instructs a computer on how to load its operating system, with a ransomware note. In reality, the note is a misdirection and the malware destroys the MBR and targeted files [10].  

One of the most notable groups that used wiper malware was Sandworm. Sandworm is an APT attributed to Russia’s foreign military intelligence agency, GRU. The group has been active since 2009 and has used a variety of TTPs within their attacks. They have a history of targeting Ukraine including attacks in 2015 on Ukraine’s energy distribution companies and in 2017 when they used the aforementioned NotPetya malware against several Ukrainian organizations [11]. Another Russian (or pro-Russian) group using wiper malware to target Ukraine is DEV-0586. This group targeted various Ukrainian organizations in January 2022 with Whispergate wiper malware. This type of wiper malware presents itself as ransomware by displaying a file instructing the victim to pay Bitcoin to have their files decrypted [12].  

Darktrace did not observe any confirmed cases of HermeticWiper nor other conflict-associated wipers (e.g IsaacWiper and CaddyWiper) within the customer base over this period. Despite this, Darktrace DETECT has a variety of models related to wipers and data destruction:

Darktrace Detections for T1485 (Data Destruction)- this is the main technique exploited during wiper attacks

- Unusual Activity / Anomalous SMB Delete Volume

- IaaS / Unusual Activity / Anomalous AWS Resources Deleted

- IaaS / Storage / S3 Bucket Delete

- SaaS / Resource / Mass Email Deletes from Rare Location

- SaaS / Resource / Anomalous SaaS Resources Deleted

- SaaS / Resource / Resource Permanent Delete

- [If RESPOND is enabled] Antigena / Network / Manual / Enforce Pattern of Life

- [If RESPOND is enabled] Antigena / SaaS / Antigena Unusual Activity Block

Tactic 3: Spear-Phishing

Another strategy that some threat actors employ is spear-phishing. Targeting can be done using email, social media, messaging, or other platforms.

The hacking group Armageddon (also known as Gamaredon) has been responsible for several spear-phishing attacks during the crisis, primarily targeting individuals involved in the Ukrainian Government [13]. Since the beginning of the war, the group has been sending out a large volume of emails containing an HTML file which, if opened, downloads and launches a RAR payload. Those who click the attached link download an HTA with a PowerShell script which obtains the final Armageddon payload. Using the same strategy, the group is also targeting governmental agencies in the European Union [14]. With high-value targets, the need to improve teaching around phishing identification to minimize the chance of being caught in an attacker's net is higher than ever. 

In comparison to the wider trends, Darktrace analysts again saw little-to-no evidence of conflict-associated phishing campaigns affecting customers. Those phishing attempts which did target customers were largely not conflict-related. In some cases, the conflict was used opportunistically, such as when one customer was targeted with a phishing email referencing Russian bank exclusions from the SWIFT payment system (Figures 1 and 2). The email was identified by Darktrace/Email as a probable attempt at financial extortion and inducement - in this case the company received a spoofed email from a major bank’s remittance department.  

Figure 1- Screencap of targeted phishing email sent to Darktrace customer
Figure 2- Attached file contains soliciting reference to SWIFT, a money payment system which select Russian banks were removed from because of the conflict [15]

 Although the conflict was used as a reference in some examples, in most of Darktrace’s observed phishing cases during the conflict period there was little-to-no evidence to suggest that the company being targeted nor the threat actor behind the phishing attempt was associated with or attributable to the Russia-Ukraine conflict.

However, Darktrace/Email has several model categories which pick up phishing related threats:

Sample of Darktrace for Email Detections for T1566 (Phishing)- this is the overarching technique exploited during spear-phishing events

Model Categories:

- Inducement

- Internal / External User Spoofing

- Internal / External Domain Spoofing

- Fake Support

- Link to Rare Domains

- Link to File Storage

- Redirect Links

- Anomalous / Malicious Attachments

- Compromised Known Sender

Specific models can be located on the Email Console

 

Tactic 4: Distributed-Denial-of-Service (DDoS)

Another tactic employed by both pro-Russian and pro-Ukrainian threat actors was DDoS (Distributed Denial of Service) attacks. Both pro-Russia and pro-Ukraine actors were seen targeting critical infrastructure, information resources, and governmental platforms with mass DDoS attacks. The Ukrainian Minister of Digital Transformation, Mykhailo Fedorov, called on an IT Army of underground Ukrainian hackers and volunteers to protect Ukraine's critical infrastructure and conduct DDoS attacks against Russia [16]. As of 1 August 2022, more than two hundred thousand people are subscribed to the group's official Telegram channel, where potential DDoS targets are announced [17].

Darktrace observed similar pro-Ukraine DDoS behaviors within a variety of customer environments. These DDoS campaigns appeared to involve low-volume individual support combined with crowd-sourced DDoS activity. They were hosted on a range of public-sourced DDoS sites and seemed to share sentiments of groups such as the IT Army of Ukraine (Figure 3).

Figure 3- Example DDoS outsource domain with unusual TLD 

From the Russian side, one of the prominent newly emerged groups, Killnet, is striking back, launching several massive DDoS attacks against the critical infrastructure of countries that provide weaponry to Ukraine [18 & 19]. Today, the number of supporters of Killnet has grown to eighty-four thousand on their Telegram channel. The group has already launched a number of mass attacks on several NATO states, including Germany, Poland, Italy, Lithuania and Norway. This shows the conflict has attracted new and fast-growing groups with large backing and the capacity to undertake widespread attacks. 

DETECT has several models to identify anomalous DoS/DDoS activity:

Darktrace Detection for T1498 (Network Denial of Service)- this is the main technique exploited during DDoS attacks

- Device / Anomaly Indicators / Denial of Service Activity Indicator

- Anomalous Server Activity / Possible Denial of Service Activity

- [If RESPOND is enabled] Antigena / Network / External Threat / Antigena Suspicious Activity Block

What did Darktrace observe?

Darktrace’s cross-fleet detections were largely contrary to expectations. Analysts did not see large-scale complex conflict-linked attacks utilizing either conflict-associated ransomware, malware, or other TTPs. Instead, cyber incidents observed were largely opportunistic, using malware that could be purchased through Malware-as-a-Service models and other widely available toolkits, (rather than APT or conflict-attributable attacks). Overall, this is not to say there have been no repercussions from the conflict or that opportunistic attacks will cease, but evidence suggests that there were fewer wider cyber consequences beyond the initial APT-based attacks seen in the public forum. 

Another trend expected since the beginning of the conflict was targeted responses to sanction announcements focusing on NATO businesses and governments. Analysts, however, saw the limited reactive actions, with little-to-no direct impact from sanction announcements. Although cyber-attacks on some NATO organizations did take place, they were not as widespread or impactful as expected. Lastly, it was thought that exposure to new and sophisticated exploits would increase and be used to weaken NATO nations - especially corporations in critical industries. However, analysts observed relatively common exploits deployed indiscriminately and opportunistically. Overall, with the wider industry expecting chaos, Darktrace analysts did not see the crisis taken advantage of to target wider businesses outside of Ukraine. Based on this comparison between expectations and reality, the conflict has demonstrated the danger of  falling prey to confirmation bias and the need to remain vigilant and expect the unexpected. It may be possible to say that cyberwar is ‘cold’ right now, however the element of surprise is always present, and it is better to be prepared to protect yourself and your organization.    

What to Expect from the Future

As cyberattacks continue to become less monetarily and physically costly, it is to be expected that they will increase in frequency. Even after a political ceasefire is established, hacking groups can harbour resentment and continue their attacks, though possibly on a smaller scale.  

Additionally, the longer this conflict continues, the more sophisticated hacking groups’s attacks may become. In one of their publications, Killnet shared with subscribers that they had created ‘network weaponry’ powerful enough to simultaneously take down five European countries (Figure 4) [20]. Whether or not this claim is true, it is vital to be prepared. The European Union and the United States have supported Ukraine since the start of the invasion, and the EU has also stated that it is considering providing further assistance to help Ukraine in cyberspace [21].

Figure 4- Snapshot of Killnet Telegram announcement

How to Protect Against these Attacks

In the face of wider conflict and cybersecurity tensions, it is crucial that organizations evaluate their security stack and practise the following: 

·       Know what your critical assets are and what software is running on them. 

·       Keep your software up to date. Prioritize patching critical and high vulnerabilities that allow remote code execution. 

·       Enforce Multifactor Authentication (MFA) to the greatest extent possible. 

·       Require the use of a password manager to generate strong and unique passwords for each separate account. 

·       Backup all the essential files on the cloud and external drives and regularly maintain them. 

·       Train your employees to recognize phishing emails, suspicious websites, infected links or other abnormalities to prevent successful compromise of email accounts. 

In order to prevent an organization from suffering damage due to one of the attacks mentioned above, a full-circle approach is needed. This defence starts with a thorough understanding of the attack surface to provide timely mitigation. This can be supported by Darktrace products: 

·       As shown throughout this blog, Darktrace DETECT and Darktrace/Email have several models relating to conflict-associated TTPs and attacks. These help to quickly alert security teams and provide visibility of anomalous behaviors.

·       Darktrace PREVENT/ASM helps to identify vulnerable external-facing assets. By patching and securing these devices, the risk of exploit is drastically reduced.

·       Darktrace RESPOND and RESPOND/Email can make targeted actions to a range of threats such as blocking incoming DDoS connections or locking malicious email links.

Thanks to the Darktrace Threat Intelligence Unit for their contributions to this blog.

Appendices 

Reference List

[1] https://www.atlanticcouncil.org/blogs/ukrainealert/vladimir-putins-ukraine-invasion-is-the-worlds-first-full-scale-cyberwar/ 

[2] https://www.reuters.com/article/us-ukraine-cybersecurity-idUSKCN0VY30K

[3] https://www.reuters.com/article/us-ukraine-cybersecurity-sandworm-idUSKBN0UM00N20160108

[4 & 11] https://www.wired.com/story/notpetya-cyberattack-ukraine-russia-code-crashed-the-world/ 

[5] https://www.scmagazine.com/analysis/ransomware/despite-hopes-for-decline-ransomware-attacks-increased-during-russia-ukraine-conflict

[6] https://ransomware.org/blog/has-the-ukraine-conflict-disrupted-ransomware-attacks/

[7] https://www.cfr.org/blog/financial-incentives-may-explain-perceived-lack-ransomware-russias-latest-assault-ukraine

[8] https://www.bleepingcomputer.com/news/security/hackers-use-contis-leaked-ransomware-to-attack-russian-companies/ 

[9] https://voi.id/en/technology/138937/hermetica-owner-from-cyprus-didnt-know-his-server-was-used-in-malicious-malware-attack-in-ukraine 

[10] https://www.reuters.com/article/ukraine-crisis-cyber-cyprus-idCAKBN2KT2QI 

[11] https://www.eset.com/int/about/newsroom/press-releases/research/eset-research-ukraine-hit-by-destructive-attacks-before-and-during-the-russian-invasion-with-hermet/ 

[12] https://blog.malwarebytes.com/threat-intelligence/2022/03/hermeticwiper-a-detailed-analysis-of-the-destructive-malware-that-targeted-ukraine/ 

[13] https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/ 

[15] https://www.cisa.gov/uscert/ncas/alerts/aa22-057a 

[16] https://attack.mitre.org/groups/G0047/ 

[17] https://cyware.com/news/ukraine-cert-warns-of-increasing-attacks-by-armageddon-group-850081f8 

[18] https://www.bbc.co.uk/news/business-60521822

[19] https://foreignpolicy.com/2022/04/11/russia-cyberwarfare-us-ukraine-volunteer-hackers-it-army/

[20] https://t.me/itarmyofukraine2022

[21] https://www.csoonline.com/article/3664859/russian-ddos-attack-on-lithuania-was-planned-on-telegram-flashpoint-says.html

[19 & 20] https://flashpoint.io/blog/killnet-kaliningrad-and-lithuanias-transport-standoff-with-russia/ 

[21] https://presidence-francaise.consilium.europa.eu/en/news/member-states-united-in-supporting-ukraine-and-strengthening-the-eu-s-telecommunications-and-cybersecurity-resilience/ 

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
Rosa Jong
OSINT Analyst
Written by
Taisiia Garkava
Security 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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