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September 13, 2023

How Darktrace Stopped Akira Ransomware

Learn how Darktrace is uniquely placed to identify and contain the novel Akira ransomware strain, first observed in March 2023.
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
Manoel Kadja
Cyber Analyst
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13
Sep 2023

Introduction to Akira Ransomware

In the face of a seemingly never-ending production line of novel ransomware strains, security teams across the threat landscape are continuing to see a myriad of new variants and groups targeting their networks. Naturally, new strains and threat groups present unique challenges to organizations. The use of previously unseen tactics, techniques, and procedures (TTPs) means that threat actors can often completely bypass traditional rule and signature-based security solutions, thus rendering an organization’s digital environment vulnerable to attack.

What is Akira Ransomware?

One such example of a novel ransomware family is Akira, which was first observed in the wild in March 2023. Much like many other strains, Akira is known to target corporate networks worldwide, encrypting sensitive files and demanding huge sums of money to retrieve the data and stop it from being posted online [1].

Key characteristics of Akira Ransomware

  • Targeted Attacks: Focuses on specific industries and organizations, often targeting those with valuable data.
  • Double Extortion Tactics: Employs double extortion by encrypting data and threatening to release it publicly if the ransom is not paid.
  • Advanced Encryption: Utilizes sophisticated encryption algorithms to ensure that data recovery is impossible without the decryption key.
  • Custom Ransom Notes: Delivers personalized ransom notes tailored to the victim, often containing detailed instructions and specific payment demands.
  • Stealth Techniques: Uses advanced evasion techniques to avoid detection by security tools and to remain undetected for extended periods.
  • Fast Encryption Process: Known for its rapid encryption process, minimizing the time window for detection and response by the victim.
  • Frequent Updates: Regularly updates its malware to bypass the latest security defenses and to improve its effectiveness.
  • Professional Communication: Maintains professional and often polite communication with victims to facilitate ransom payments and decryption.

Darktrace AI capabilities detect Akira Ransomware

In late May 2023, Darktrace observed multiple instances of Akira ransomware affecting networks across its customer base. Thanks to its anomaly-based approach to threat detection, Darktrace successfully identified the novel ransomware attacks and provided full visibility over the cyber kill chain, from the initial compromise to the eventual file encryptions and ransom notes. In cases where Darktrace was enabled in autonomous response mode, these attacks were mitigated the early stages of the attack, thus minimizing any disruption or damage to customer networks.

Initial access and privileged escalation

Methods used by Akira ransomware for privileged escalation

The Akira ransomware group typically uses spear-phishing campaigns containing malicious downloads or links as their primary initial access vector; however, they have also been known to use Remote Desktop Protocol (RDP) brute-force attacks to access target networks [2].

While Darktrace did observe the early access activities that are detailed below, it is very likely that the actual initial intrusion happened prior to this, through targeted phishing attacks that fell outside of Darktrace’s purview. The first indicators of compromise (IoCs) that Darktrace observed on customer networks affected by Darktrace were typically unusual RDP sessions, and the use of compromised administrative credentials.

Darktrace detection of initial access and priviledged escalation

On one Darktrace customer’s network (customer A), Darktrace identified a highly privileged credential being used for the first time on an internal server on May 21, 2023. Around a week later, this server was observed establishing RDP connections with multiple internal destination devices via port 3389. Further investigation carried out by the customer revealed that this credential had indeed been compromised. On May 30, Darktrace detected another device scanning internal devices and repeatedly failing to authenticate via Kerberos.

As the customer had integrated Darktrace with Microsoft Defender, their security team received additional cyber threat intelligence from Microsoft which, coupled with the anomaly alerts provided by Darktrace, helped to further contextualize these anomalous events. One specific detail gleaned from this integration was that the anomalous scanning activity and failed authentication attempts were carried out using the compromised administrative credentials mentioned earlier.

By integrating Microsoft Defender with Darktrace, customers can efficiently close security gaps across their digital infrastructure. While Darktrace understands customer environments and provides valuable network-level insights, by integrating with Microsoft Defender, customers can further enrich these insights with endpoint-specific information and activity.

In another customer’s network (customer B), Darktrace detected a device, later observed writing a ransom note, receiving an unusual RDP connection from another internal device. The RDP cookie used during this activity was an administrative RDP cookie that appeared to have been compromised. This device was also observed making multiple connections to the domain, api.playanext[.]com, and using the user agent , AnyDesk/7.1.11, indicating the use of the AnyDesk remote desktop service.

Although this external domain does not appear directly related to Akira ransomware, open-source intelligence (OSINT) found associations with multiple malicious files, and it appeared to be associated with the AnyDesk user agent, AnyDesk/6.0.1 [3]. The connections to this endpoint likely represented the malicious use of AnyDesk to remotely control the customer’s device, rather than Akira command-and-control (C2) infrastructure or payloads. Alternatively, it could be indicative of a spoofing attempt in which the threat actor is attempting to masquerade as legitimate remote desktop service to remain undetected by security tools.

Around the same time, Darktrace observed many devices on customer B’s network making anomalous internal RDP connections and authenticating via Kerberos, NTLM, or SMB using the same administrative credential. These devices were later confirmed to be affected by Akira Ransomware.

Figure 1 shows how Darktrace detected one of those internal devices failing to login via SMB multiple times with a certain credential (indication of a possible SMB/NTLM brute force), before successfully accessing other internal devices via SMB, NTLM and RDP using the likely compromised administrative credential mentioned earlier.

Figure 1: Model Breach Event Log indicating unusual SMB, NTLM and RDP activity with different credentials detected which led to the Darktrace model breaches, "Unusual Admin RDP Session” and “Successful Admin Brute-Force Activity”.

Darktrace models observed for initial access and privilege escalation:

  • Device / Anomalous RDP Followed By Multiple Model Breaches
  • Anomalous Connection / Unusual Admin RDP Session
  • New Admin Credentials on Server
  • Possible SMB/NTLM Brute Force Indicator
  • Unusual Activity / Successful Admin Brute-Force Activity

Internal Reconnaissance and Lateral Movement

The next step Darktrace observed during Akira Ransomware attacks across the customer was internal reconnaissance and lateral movement.

How Akira Ransomware conducts internal reconnaissance

In another customer’s environment (customer C), after authenticating via NTLM using a compromised credential, a domain controller was observed accessing a large amount of SMB shares it had never previously accessed. Darktrace understood that this SMB activity represented a deviation in the device’s expected behavior and recognized that it could be indicative of SMB enumeration. Darktrace observed the device making at least 196 connections to 34 unique internal IPs via port 445. SMB actions read, write, and delete were observed during those connections. This domain controller was also one of many devices on the customer’s network that was received incoming connections from an external endpoint over port 3389 using the RDP protocol, indicating that the devices were likely being remotely controlled from outside the network. While there were no direct OSINT links with this endpoint and Akira ransomware, the domain controller in question was later confirmed to be compromised and played a key role in this phase of the attack.

Moreover, this represents the second IoC that Darktrace observed that had no obvious connection to Akira, likely indicating that Akira actors are establishing entirely new infrastructure to carry out their attacks, or even utilizing newly compromised legitimate infrastructure. As Darktrace adopts an anomaly-based approach to threat detection, it can recognize suspicious activity indicative of an emerging ransomware attack based on its unusualness, rather than having to rely on previously observed IoCs and lists of ‘known-bads’.

Darktrace further observed a flurry of activity related to lateral movement around this time, primarily via SMB writes of suspicious files to other internal destinations. One particular device on customer C’s network was detected transferring multiple executable (.exe) and script files to other internal devices via SMB.

Darktrace recognized that these transfers represented a deviation from the device’s normal SMB activity and may have indicated threat actors were attempting to compromise additional devices via the transfer of malicious software.

Figure 2: Advanced Search results showing 20 files associated with suspicious SMB write activity, amongst them executable files and dynamic link libraries (DLLs).

Darktrace DETECT models observed for internal reconnaissance and lateral movement:

  • Device / RDP Scan
  • Anomalous Connection / SMB Enumeration
  • Anomalous Connection / Possible Share Enumeration Activity
  • Scanning of Multiple Devices (Cyber AI Analyst Incident)
  • Device / Possible SMB/NTLM Reconnaissance
  • Compliance / Incoming Remote Desktop
  • Compliance / Outgoing NTLM Request from DC
  • Unusual Activity / Internal Data Transfer
  • Security Integration / Lateral Movement and Integration Detection
  • Device / Anomalous SMB Followed By Multiple Model Breaches

Ransomware deployment

In the final phase of Akira ransomware attacks detected on Darktrace customer networks, Darktrace identified the file extension “.akira” being added after encryption to a variety of files on the affected network shares, as well as a ransom note titled “akira_readme.txt” being dropped on affected devices.

On customer A’s network, after nearly 9,000 login failures and 2,000 internal connection attempts indicative of scanning activity, one device was detected transferring suspicious files over SMB to other internal devices. The device was then observed connecting to another internal device via SMB and continuing suspicious file activity, such as appending files on network shares with the “.akira” extension, and performing suspicious writes to SMB shares on other internal devices.

Darktrace’s autonomous threat investigator, Cyber AI Analyst™, was able to analyze the multiple events related to this encryption activity and collate them into one AI Analyst incident, presenting a detailed and comprehensive summary of the entire incident within 10 minutes of Darktrace’s initial detection. Rather than simply viewing individual breaches as standalone activity, AI Analyst can identify the individual steps of an ongoing attack to provide complete visibility over emerging compromises and their kill chains. Not only does this bolster the network’s defenses, but the autonomous investigations carried out by AI Analyst also help to save the security team’s time and resources in triaging and monitoring ongoing incidents.

Figure 3: Darktrace Cyber AI Analyst incident correlated multiple model breaches together to show Akira ransomware encryption activity.

In addition to analyzing and compiling Darktrace model breaches, AI Analyst also leveraged the host-level insights provided by Microsoft Defender to enrich its investigation into the encryption event. By using the Security Integration model breaches, AI Analyst can retrieve timestamp and device details from a Defender alert and further investigate any unusual activity surrounding the alert to present a full picture of the suspicious activity.

In customer B’s environment, following the unusual RDP sessions and rare external connections using the AnyDesk user agent, an affected device was later observed writing around 2,000 files named "akira_readme.txt" to multiple internal SMB shares. This represented the malicious actor dropping ransom notes, containing the demands and extortion attempts of the actors.

Figure 4: Model Breach Event Log indicating the ransom note detected on May 12, 2023, which led to the Darktrace DETECT model breach, Anomalous Server Activity / Write to Network Accessible WebRoot.
Figure 5: Packet Capture (PCAP) demonstrating the Akira ransom note captured from the connection details seen in Figure 4.

As a result of this ongoing activity, an Enhanced Monitoring model breach, a high-fidelity detection model type that detects activities that are more likely to be indicative of compromise, was escalated to Darktrace’s Security Operations Center (SOC) who, in turn were able to further investigate and triage this ransomware activity. Customers who have subscribed to Darktrace’s Proactive Threat Notification (PTN) service would receive an alert from the SOC team, advising urgent follow up action.

Darktrace detection models observed during ransomware deployment:

  • Security Integration / Integration Ransomware Incident
  • Security Integration / High Severity Integration Detection
  • Security Integration / Integration Ransomware Detected
  • Device / Suspicious File Writes to Multiple Hidden SMB Shares
  • Compliance / SMB Drive Write
  • Compromise / Ransomware / Suspicious SMB Activity (Proactive Threat Notification Alerted by the Darktrace SOC)
  • Anomalous File / Internal / Additional Extension Appended to SMB File
  • Anomalous File / Internal / Unusual SMB Script Write
  • Compromise / Ransomware / Ransom or Offensive Words Written to SMB
  • Anomalous Server Activity /Write to Network Accessible WebRoot
  • Anomalous Server Activity /Write to Network Accessible WebRoot

Darktrace autonomous response neutralizes Akira Ransomware

When Darktrace is configured in autonomous response mode, it is able to follow up successful threat identifications with instant autonomous actions that stop malicious actors in their tracks and prevent them from achieving their end goals.

In the examples of Darktrace customers affected by Akira Ransomware outlined above, only customer A had autonomous response mode enabled during their ransomware attack. The autonomous response capability of Darktrace helped the customer to minimize disruption to the business through multiple targeted actions on devices affected by ransomware.

One action carried out by Darktrace's Autonomous Respose was to block all on-going traffic from affected devices. In doing so, Darktrace effectively shuts down communications between devices affected by Akira and the malicious infrastructure used by threat actors, preventing the spread of data on the client network or threat actor payloads.

Another crucial response action applied on this customer’s network was combat Akira was to “Enforce a Pattern of Life” on affected devices. This action is designed to prevent devices from performing any activity that would constitute a deviation from their expected behavior, while allowing them to continue their ‘usual’ business operations without causing any disruption.

While the initial intrusion of the attack on customer A’s network likely fell outside of the scope of Darktrace’s visibility, Darktrace was able to minimize the disruption caused by Akira, containing the ransomware and allowing the customer to further investigate and remediate.

Darktrace Autonomous Response model breaches:

  • Antigena / Network / External Threat / Antigena Ransomware Block
  • Antigena / Network / External Threat / Antigena Suspicious Activity Block
  • Antigena / Network / Significant Anomaly / Antigena Enhanced Monitoring from Server Block
  • Antigena / Network / External Threat / Antigena Suspicious Activity Block
  • Antigena / Network / External Threat / Antigena File then New Outbound Block
  • Antigena / Network / Insider Threat / Antigena Unusual Privileged User Activities Block
  • Antigena / Network / Significant Anomaly / Antigena Breaches Over Time Block
  • Antigena / Network / Significant Anomaly / Antigena Significant Anomaly from Client Block
  • Antigena / Network /Insider Threat /Antigena SMB Enumeration Block

Conclusion

The impact of cyber attacks

Novel ransomware strains like Akira Ransomware present a significant challenge to security teams across the globe due to the constant evolution of attack methods and tactics, making it huge a challenge for security teams to stay up to date with the most current threat intelligence.  

Therefore, it is paramount for organizations to adopt a technology designed around an intelligent decision maker able to identify unusual activity that could be indicative of a ransomware attack without depending solely on rules, signatures, or statistic lists of malicious IoCs.

Importance of AI-powered cybersecurity solutions

Darktrace identified Akira ransomware at every stage of the attack’s kill chain on multiple customer networks, even when threat actors were utilizing seemingly legitimate services (or spoofed versions of them) to carry out malicious activity. While this may have gone unnoticed by traditional security tools, Darktrace’s anomaly-based detection enabled it to recognize malicious activity for what it was. When enabled in autonomous response mode, Darktrace is able to follow up initial detections with machine-speed preventative actions to stop the spread of ransomware and minimize the damage caused to customer networks.  

There is no silver bullet to defend against novel cyber-attacks, however Darktrace’s anomaly-based approach to threat detection and autonomous response capabilities are uniquely placed to detect and respond to cyber disruption without latency.

Credit to: Manoel Kadja, Cyber Analyst, Nahisha Nobregas, SOC Analyst.

Appendices

IOC - Type - Description/Confidence

202.175.136[.]197 - External destination IP -Incoming RDP Connection

api.playanext[.]com - External hostname - Possible RDP Host

.akira - File Extension - Akira Ransomware Extension

akira_readme.txt - Text File - Akira Ransom Note

AnyDesk/7.1.11 - User Agent -AnyDesk User Agent

MITRE ATT&CK Mapping

Tactic & Technique

DISCOVERY

T1083 - File and Directory Discovery

T1046 - Network Service Scanning

T1135 - Network Share Discovery

RECONNAISSANCE

T1595.002 - Vulnerability Scanning

CREDENTIAL ACCESS, COLLECTION

T1557.001 - LLMNR/NBT-NS Poisoning and SMB Relay

DEFENSE EVASION, LATERAL MOVEMENT

T1550.002 - Pass the Hash

DEFENSE EVASION, PERSISTENCE, PRIVILEGE ESCALATION, INITIAL ACCESS

T1078 - Valid Accounts

DEFENSE EVASION

T1006 - Direct Volume Access

LATERAL MOVEMENT

T1563.002 - RDP Hijacking

T1021.001 - Remote Desktop Protocol

T1080 - Taint Shared Content

T1021.002 - SMB/Windows Admin Shares

INITIAL ACCESS

T1190 - Exploit Public-Facing Application

T1199 - Trusted Relationship

PERSISTENCE, INITIAL ACCESS

T1133 - External Remote Services

PERSISTENCE

T1505.003 - Web Shell

IMPACT

T1486 - Data Encrypted for Impact

References

[1] https://www.bleepingcomputer.com/news/security/meet-akira-a-new-ransomware-operation-targeting-the-enterprise/

[2] https://www.civilsdaily.com/news/cert-in-warns-against-akira-ransomware/#:~:text=Spread%20Methods%3A%20Akira%20ransomware%20is,Desktop%20connections%20to%20infiltrate%20systems

[3] https://hybrid-analysis.com/sample/0ee9baef94c80647eed30fa463447f000ec1f50a49eecfb71df277a2ca1fe4db?environmentId=100

Get the latest insights on emerging cyber threats

This report explores the latest trends shaping the cybersecurity landscape and what defenders need to know in 2026.

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
Manoel Kadja
Cyber Analyst

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May 19, 2026

State of AI Cybersecurity 2026: 77% of security stacks include AI, but trust is lagging

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Findings in this blog are taken from Darktrace’s annual State of AI Cybersecurity Report 2026.

AI is a contributing member of nearly every modern cybersecurity team. As we discussed earlier in this blog series, rapid AI adoption is expanding the attack surface in ways that security professionals have never before experienced while also empowering attackers to operate at unprecedented speed and scale. It’s only logical that defenders are harnessing the power of AI to fight back.

After all, AI can help cybersecurity teams spot the subtle signs of novel threats before humans can, investigate events more quickly and thoroughly, and automate response. But although AI has been widely adopted, this technology is also frequently misunderstood, and occasionally viewed with suspicion.

For CISOs, the cybersecurity marketplace can be noisy. Making sense of competing vendors’ claims to distinguish the solutions that truly deliver on AI’s full potential from those that do not isn’t always easy. Without a nuanced understanding of the different types of AI used across the cybersecurity stack, it is difficult to make informed decisions about which vendors to work with or how to gain the most value from their solutions. Many security leaders are turning to Managed Security Service Providers (MSSPs) for guidance and support.

The right kinds of AI in the right places?

Back in 2024, when we first conducted this annual survey, more than a quarter of respondents were only vaguely familiar with generative AI or hadn’t heard of it at all. Today, GenAI plays a role in 77% of security stacks. This percentage marks a rapid increase in both awareness and adoption over a relatively short period of time.

According to security professionals, different types of AI are widely integrated into cybersecurity tooling:

  • 67% report that their organization’s security stack uses supervised machine learning
  • 67% report that theirs uses agentic AI
  • 58% report that theirs uses natural language processing (NLP)
  • 35% report that theirs uses unsupervised machine learning

But their responses suggest that organizations aren’t always using the most valuable types of AI for the most relevant use cases.

Despite all the recent attention AI has gotten, supervised machine learning isn’t new. Cybersecurity vendors have been experimenting with models trained on hand-labeled datasets for over a decade. These systems are fed large numbers of examples of malicious activity – for instance, strains of ransomware – and use these examples to generalize common indicators of maliciousness – such as the TTPs of multiple known ransomware strains – so that the models can identify similar attacks in the future. This approach is more effective than signature-based detection, since it isn’t tied to an individual byte sequence or file hash. However, supervised machine learning models can miss patterns or features outside the training data set. When adversarial behavior shifts, these systems can’t easily pivot.

Unsupervised machine learning, by contrast, can identify key patterns and trends in unlabeled data without human input. This enables it to classify information independently and detect anomalies without needing to be taught about past threats. Unsupervised learning can continuously learn about an environment and adapt in real time.

One key distinction between supervised and unsupervised machine learning is that supervised learning algorithms require periodic updating and re-training, whereas unsupervised machine learning trains itself while it works.

The question of trust

Even as AI moves into the mainstream, security professionals are eyeing it with a mix of enthusiasm and caution. Although 89% say they have good visibility into the reasoning behind AI-generated outputs, 74% are limiting AI’s ability to take autonomous action in their SOC until explainability improves. 86% do not allow AI to take even small remediation actions without human oversight.

This model, commonly known as “human in the loop,” is currently the norm across the industry. It seems like a best-of-both-worlds approach that allows teams to experience the benefits of AI-accelerated response without relinquishing control – or needing to trust an AI system.

Keeping humans somewhat in the loop is essential for getting the best out of AI. Analysts will always need to review alerts, make judgement calls, and set guardrails for AI's behavior. Their input helps AI models better understand what “normal” looks like, improving their accuracy over time.

However, relying on human confirmation has real costs – it delays response, increases the cognitive burden analysts must bear, and creates potential coverage gaps when security teams are overwhelmed or unavailable. The traditional model, in which humans monitor and act on every alert, is no longer workable at scale.

If organizations depend too heavily on in-the-loop humans, they risk recreating the very problem AI is meant to solve: backlogs of alerts waiting for analyst review. Removing the human from the loop can buy back valuable time, which analysts can then invest in building a proactive security posture. They can also focus more closely on the most critical incidents, where human attention is truly needed.

Allowing AI to operate autonomously requires trust in its decision-making. This trust can be built gradually over time, with autonomous operations expanding as trust grows. But it also requires knowledge and understanding of AI — what it is, how it works, and how best to deploy it at enterprise scale.

Looking for help in all the right places

To gain access to these capabilities in a way that’s efficient and scalable, growing numbers of security leaders are looking for outsourced support. In fact, 85% of security professionals prefer to obtain new SOC capabilities in the form of a managed service.

This makes sense: Managed Security Service Providers (MSSPs) can deliver deep, continuously available expertise without the cost and complexity of building an in-house team. Outsourcing also allows organizations to scale security coverage up or down as needs change, stay current with evolving threats and regulatory requirements, and leverage AI-native detection and response without needing to manage the AI tools themselves.

Preferences for MSSP-delivered security operations are particularly strong in the education, energy (87%), and healthcare sectors. This makes sense: all are high-value targets for threat actors, and all tend to have limited cybersecurity budgets, so the need for a partner who can deliver affordable access to expertise at scale is strong. Retailers also voiced a strong preference for MSSP-delivered services. These companies are tasked with managing large volumes of consumer personal and financial data, and with transforming an industry traditionally thought of as a late adopter to a vanguard of cyber defense. Technology companies, too, have a marked preference for SOC capabilities delivered by MSSPs. This may simply be because they understand the complexity of the threat landscape – and the advantages of specialized expertise — so well.

In order to help as many organizations as possible – from major enterprises to small and midmarket companies – benefit from enterprise-grade, AI-native security, Darktrace is making it easier for MSSPs to deliver its technology. The ActiveAI Security Portal introduces an alert dashboard designed to increase the speed and efficiency of alert triage, while a new AI-powered managed email security solution is giving MSSPs an edge in the never-ending fight against advanced phishing attacks – helping partners as well as organizations succeed on the frontlines of cyber defense.

Explore the full State of AI Cybersecurity 2026 report for deeper insights into how security leaders are responding to AI-driven risks.

Learn more about securing AI in your enterprise.

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May 18, 2026

AI Insider Threats: How Generative AI is Changing Insider Risk

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How generative AI changes insider behavior

AI systems, especially generative platforms such as chatbots, are designed for engagement with humans. They are equipped with extraordinary human-like responses that can both confirm, and inflate, human ideas and ideology; offering an appealing cognitive partnership between machine and human.  When considering this against the threat posed by insiders, the type of diverse engagement offered by AI can greatly increase the speed of an insider event, and can facilitate new attack platforms to carry out insider acts.  

This article offers analysis on how to consider this new paradigm of insider risk, and outlines key governance principles for CISOs, CSOs and SOC managers to manage the threats inherent with AI-powered insider risk.

What is an insider threat?

There are many industry or government definitions of what constitutes insider threat. At its heart, it relates to the harm created when trusted access to sensitive information, assets or personnel is abused bywith malicious intent, or through negligent activities.  

Traditional methodologies to manage insider threat have relied on two main concepts: assurance of individuals with access to sensitive assets, and a layered defense system to monitor for any breach of vulnerability. This is often done both before, and after access has been granted.  In the pre-access state, assurance is gained through security or recruitment checks. Once access is granted, controls such as privileged access, and zero-trust architecture offer defensive layers.

How does AI change the insider threat paradigm?

While these two concepts remain central to the management of insider threats, the introduction of AI offers three key new aspects that will re-shape the paradigm:.  

AI can act as a cognitive amplifier, influencing and affecting the motivations that can lead to insider-related activity. This is especially relevant for the deliberate insider - someone who is considering an act of insider harm. These individuals can now turn to AI systems to validate their thinking, provide unique insights, and, crucially, offer encouragement to act. With generative systems hard-wired to engage and agree with users, this can turn a helpful AI system into a dangerous AI hype machine for those with harmful insider intent.  

AI can act as an operational enabler. AI can now develop and increase the range of tools needed to carry out insider acts. New social engineering platforms such as vishing and deepfakes give adversaries a new edge to create insider harm. AI can generate solutions and operational platforms at increasing speeds; often without the need for human subject matter expertise to execute the activities. As one bar for advanced AI capabilities continues to be raised, the bar needed to make use of those platforms has become significantly lower.

AI can act as a semi-autonomous insider, particularly when agentic AI systems or non-human identities are provided broad levels of autonomy; creating a vector of insider acts with little-to-no human oversight or control. As AI agents assume many of the orchestration layers once reserved for humans, they do so without some of the restricted permissions that generally bind service accounts. With broad levels of accessibility and authority, these non-human identities (NHIs) can themselves become targets of insider intent.  Commonly, this refers to the increasing risks of prompt injection, poisoning, or other types of embedded bias. In many ways, this mirrors the risks of social engineering traditionally faced by humans. Even without deliberate or malicious efforts to corrupt them, AI systems and AI agents can carry out unintended actions; creating vulnerabilities and opportunities for insider harm.

How to defend against AI-powered insider threats

The increasing attack surfaces created or facilitated by AI is a growing concern.  In Darktrace’s own AI cybersecurity research, the risks introduced, and acknowledged, through the proliferation of AI tools and systems continues to outstrip traditional policies and governance guardrails. 22% of respondents in the survey cited ‘insider misuse aided by generative AI’ as a major threat concern.  And yet, in the same survey, only 37% of all respondents have formal policies in place to manage the safe and responsible use of AI.  This draws a significant and worrying delta between the known risks and threat concerns, and the ability (and resources) to mitigate them.

What can CISOs and SOC leaders do to protect their organization from AI insider threats?  

Given the rapid adaptation, adoption, and scale of AI systems, implementing the right levels of AI governance is non-negotiable. Getting the correct balance between AI-driven productivity gains and careful compliance will lead to long-term benefits. Adapting traditional insider threat structures to account for newer risks posed through the use of AI will be crucial. And understanding the value of AI systems that add to your cybersecurity resilience rather than imperil it will be essential.

For those responsible for the security and protection of their business assets and data holdings, the way AI has changed the paradigm of insider threats can seem daunting.  Adopting strong, and suitable AI governance can become difficult to introduce due to the volume and complexity of systems needed to be monitored. As well as traditional insider threat mitigations such as user monitoring, access controls and active management, the speed and autonomy of some AI systems need different, as well as additional layers of control.  

How Darktrace helps protect against AI-powered insider threats

Darktrace has demonstrated that, through platforms such as our proprietary Cyber AI Analyst, and our latest product Darktrace / SECURE AI, there are ways AI systems can be self-learning, self-critical and resilient to unpredictable AI behavior whilst still offering impressive returns; complementing traditional SOC and CISO strategies to combat insider threat.  

With / SECURE AI, some of the ephemeral risks drawn through AI use can be more easily governed.  Specifically, the ability to monitor conversational prompts (which can both affect AI outputs as well as highlight potential attempts at manipulation of AI; raising early flags of insider intent); the real-time observation of AI usage and development (highlighting potential blind-spots between AI development and deployment); shadow AI detection (surfacing unapproved tools and agents across your IT stack) and; the ability to know which identities (human or non-human) have permission access. All these features build on the existing foundations of strong insider threat management structures.  

How to take a defense-in-depth approach to AI-powered insider threats

Even without these tools, there are four key areas where robust, more effective controls can mitigate AI-powered insider threat.  Each of the below offers a defencce-in-depth approach: layering acknowledgement and understanding of an insider vector with controls that can bolster your defenses.  

Identity and access controls

Having a clear understanding of the entities that can access your sensitive information, assets and personnel is the first step in understanding the landscape in which insider harm can occur.  AI has shown that it is not just flesh and bone operators who can administer insider threats; Non-Human Identities (such as agentic AI systems) can operate with autonomy and freedom if they have the right credentials. By treating NHIs in the same way as human operators (rather than helpful machine-based tools), and adding similar mitigation and management controls, you can protect both your business, and your business-based identities from insider-related attention.

Visibility and shadow AI detection

Configuring AI systems carefully, as well as maintaining internal monitoring, can help identify ‘shadow AI’ usage; defined as the use of unsanctioned AI tools within the workplace1 (this topic was researched in Darktrace’s own paper on "How to secure AI in the enterprise". The adoption of shadow AI could be the result of deliberate preference, or ‘shortcutting’; where individuals use systems and models they are familiar with, even if unsanctioned. As well as some performance risks inherent with the use of shadow AI (such as data leakage and unwanted actions), it could also be a dangerous precursor for insider-related harm (either through deliberate attempts to subvert regular monitoring, or by opening vulnerabilities through unpatched or unaccredited tooling).

Prompt and Output Guardrails

The ability to introduce guardrails for AI systems offers something of a traditional “perimeter protection” layer in AI defense architecture; checking prompts and outputs against known threat vectors, or insider threat methodologies. Alone, such traditional guardrails offer limited assurance.  But, if tied with behavior-centric threat detection, and an enforcement system that deters both malicious and accidental insider activities, this would offer considerable defense- in- depth containment.  

Forensic logging and incident readiness response

The need for detection, data capture, forensics, and investigation are inherent elements of any good insider threat strategy. To fully understand the extent or scope of any suspected insider activity (such as understanding if it was deliberate, targeted, or likely to occur again), this rich vein of analysis could prove invaluable.  As the nature of business increasingly turns ephemeral; with assets secured in remote containers, information parsed through temporary or cloud-based architecture, and access nodes distributed beyond the immediate visibility of internal security teams, the development of AI governance through containment, detection, and enforcement will grow ever more important.

Enabling these controls can offer visibility and supervision over some of the often-expressed risks about AI management. With the right kind of data analytics, and with appropriate human oversight for high-risk actions, it can illuminate the core concerns expressed through a new paradigm of AI-powered insider threats by:

  • Ensuring deliberately mis-configured AI systems are exposed through regular monitoring.
  • Highlighting changes in systems-based activity that might indicate harmful insider actions; whether malicious or accidental.
  • Promoting a secure-by-design process that discourages and deters insider-related ambitions.
  • Ensuring the control plane for identity-based access spans humans, NHIs and AI models, and:
  • Offering positive containment strategies that will help curate the extent of AI control, and minimize unwanted activities.

Why insider threat remains a human challenge

At its root, and however it has been configured, AI is still an algorithmic tool; something designed to automate, process and manage computational functions at machine speed, and boost productivity.  Even with the best cybersecurity defenses in place, the success of an insider threat management program will still depend on the ability of human operators to identify, triage, and manage the insider threat attack surface.  

AI governance policies, human-in-the-loop break points, and automated monitoring functions will not guard against acts of insider harm unless there is intention to manage this proactively, and through a strong culture of how to guard against abuses of trust and responsibility.

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About the author
Jason Lusted
AI Governance Advisor
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