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July 27, 2023

Revealing Outlaw's Returning Features & New Tactics

Darktrace's investigation of the latest Outlaw crypto-mining operation, covering the resurgence of old tactics along with the emergence of new ones.
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
Adam Potter
Senior Cyber Analyst
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27
Jul 2023

What is Outlaw Cryptocurrency Mining Operation?

The cybersecurity community has been aware of the threat of Outlaw cryptocurrency mining operation, and its affiliated activities since as early as 2018. Despite its prominence, Outlaw remains largely elusive to researchers and analysts due to its ability to adapt its tactics, procedures, and payloads.

Outlaw gained notoriety in 2018 as security researchers began observing the creation of affiliated botnets.[1][2]  Researchers gave Outlaw  its name based on the English translation of the “Haiduc” tool observed during their initial activity on compromised devices.[3],[4] By 2019, much of the initial Outlaw activity  focused on the targeting of Internet of Things (IoT) devices and other internet facing servers, reportedly focusing operations in China and on Chinese devices.[5],[6]  From the outset, mining operations featured as a core element of botnets created by the group.[7] This initial focus may have been a sign of caution by threat actors or a preliminary means of testing procedures and operation efficacy. Regardless, Outlaw actors inevitably expanded scope, targeting larger organizations and a wider range of internet facing devices across geographic scope.

Following a short period of inactivity, security researchers began to observe new Outlaw activity, showcasing additional capabilities such as the ability to kill existing crypto-mining processes on devices, thereby reclaiming devices already compromised by crypto-jacking. [8],[9]

Latest News on Outlaw

Although the more recently observed incidents of Outlaw did demonstrate some new tactics, many of its procedures remained the same, including its unique bundling of payloads that combine crypto-mining and botnet capabilities. [10] In conjunction, the continued use of mining-specific payloads and growth of affiliated botnets has bolstered the belief that Outlaw actors historically prioritizes financial gain, in lieu of overt political objectives.

Given the tendency for malicious actors to share tools and capabilities, true attribution of threat or threat group is extremely difficult in the wild. As such, a genuine survey of activity from the group across a customer base has not always been possible. Therefore, we will present an updated look into more recent activity associated with Outlaw detected across the Darktrace customer base.  

Darktrace vs Outlaw

Since late 2022, Darktrace has observed a rise in probable cyber incidents involving indicators of compromise (IoCs) associated with Outlaw. Given its continued prevalence and relative dearth of information, it is essential to take a renewed look at the latest campaign activity associated with threats like Outlaw to avoid making erroneous assumptions and to ensure the threat posed is correctly characterized.

While being aware of previous IoCs and tactics known to be employed in previous campaigns will go some way to protecting against future Outlaw attacks, it is paramount for organizations to arm themselves with an autonomous intelligent decision maker that can identify malicious activity, based on recognizing deviations from expected patterns of behavior, and take preventative action to effectively defend against such a versatile threat.

Darktrace’s anomaly-based approach to threat detection means it is uniquely positioned to detect novel campaign activity by recognizing subtle deviations in affected devices’ behavior that would have gone unnoticed by traditional security tools relying on rules, signatures and known IoCs.

Outlaw Attack Overview & Darktrace Coverage

From late 2022 through early 2023, Darktrace identified multiple cyber events involving IP addresses, domains, and payloads associated with Outlaw on customer networks. In this recent re-emergence of campaign activity, Darktrace identified numerous attack vectors and IoCs that had previously been associated with Outlaw, however it also observed significant deviations from previous campaigns.

Returning Features

As outlined in a previous blog, past iterations of Outlaw compromises include four identified, distinct phases:

1. Targeting of internet facing devices via SSH brute-forcing

2. Initiation of crypto-mining operations

3. Download of shell script and/or botnet malware payloads

4. Outgoing external SSH scanning to propagate the botnet

Nearly all affected devices analyzed by Darktrace were tagged as internet facing, as identified in previous campaigns, supporting the notion that Outlaw continues to focus on easily exposed devices. In addition to this, Darktrace observed three other core returning features from previous Outlaw campaigns in affected devices between late 2022 and early 2023:

1. Gzip and/or Script Download

2. Beaconing Activity (Command and Control)

3. Crypto-mining

Gzip and/or Script Download

Darktrace observed numerous devices downloading the Dota malware, a strain that is previously known to have been associated with the Outlaw botnet, as either a gzip file or a shell script from rare external hosts.

In some examples, IP addresses that provided the payload were flagged by open-source intelligence (OSINT) sources as having engaged in widespread SSH brute-forcing activities. While the timing of the payload transfer to the device was not consistent, download of gzip files featured prominently during directly observed or potentially affiliated activity. Moreover, Darktrace detected multiple devices performing HTTP requests for shell scripts (.sh) according to detected connection URIs. Darktrace DETECT was able to identify these anomalous connections due to the rarity of the endpoint, payloads, and connectivity for the devices.

Figure 1: Darktrace Cyber AI Analyst technical details summary from an incident during the analysis timeframe that highlights a breach device retrieving the anomalous shell scripts using wget.

Beaconing Activity – Command and Control (C2) Endpoint

Across all Outlaw activity identified by Darktrace, devices engaged in some form of beaconing behavior, rather than one-off connections to IPs associated with Outlaw. While the use of application protocol was not uniform, repeated connectivity to rare external IP addresses related to Outlaw occurred across many analyzed incidents. Darktrace’s Self-Learning AI understood that this beaconing activity represented devices deviating from their expected patterns of life and was able to bring it to the immediate attention of customer security teams.

Figure 2: Model breach log details showing sustained, repeated connectivity to Outlaw affiliated endpoint over port 443, indicating potential C2 activity.

Crypto-mining

In almost every incident of Outlaw identified across the fleet, Darktrace detected some form of cryptocurrency mining activity. Devices affected by Outlaw were consistently observed making anomalous connections to external endpoints associated with crypto-mining operations. Furthermore, the Minergate protocol appeared consistently across hosts; even when devices did not make direct crypto-mining commands, such hosts attempted connections to external entities that were known to support crypto-mining operations.

Figure 3: Advanced Search results showing a sudden spike in mining activity from a device observed connecting to Outlaw-affiliated IP addresses. Such crypto-mining activity was observed consistently across analyzed incidents.

Is Outlaw Using New Tactics?

While in the past, Outlaw activity was identified through a systematic kill chain, recent investigations conducted by Darktrace show significant deviations from this.

For instance, affected devices do not necessarily follow the previously outlined kill chain directly as they did previously. Instead, Darktrace observed affected devices exhibiting these phases in differing orders, repeating steps, or missing out attack phases entirely.

It is essential to study such variation in the kill chain to learn more about the threat of Outlaw and how threat actors are continuing to use it is varying ways. These discrepancies in kill chain elements are likely impacted by visibility into the networks and devices of Darktrace customers, with some relevant activity falling outside of Darktrace’s purview. This is particularly true for internet-exposed devices and hosts that repeatedly performed the same anomalous activity (such as making Minergate requests). Moreover, some devices involved in Outlaw activity may have already been compromised prior to Darktrace’s visibility into the network. As such, these conclusions must be evaluated with a degree of uncertainty.

SSH Activity

Although external SSH connectivity was apparent in some of the incidents detected by Darktrace, it was not directly related to brute-forcing activity. Affected devices did receive anomalous incoming SSH connections, however, wide ranging SSH failed connectivity following the initiation of mining operations by compromised devices was not readily apparent across analyzed compromises. Connections over port 22 were more frequently associated with beaconing and/or C2 activity to endpoints associated with Outlaw, than with potential brute-forcing. As such, Darktrace could not, with high confidence correlate such SSH activity to brute-forcing. This could suggest that threat actors are now portioning or rotation of botnet devices for different operations, for example dividing between botnet expansion and mining operations.

Command line tools

In cases of Outlaw investigated by Darktrace, there was also a degree of variability involving the tools used to retrieve payloads. On the networks of customers affected by Outlaw, Darktrace DETECT identified the use of user agents and command line tools that it considered to be out of character for the network and its devices.

When retrieving the Dota malware payload or shell script data, compromised devices frequently relied on numerous versions of wget and curl user agents. Although the use of such tools as a tactic cannot be definitively linked to the crypto-mining campaign, the employment of varying and/or outdated native command line tools attests to the procedural flexibility of Outlaw campaigns, and its potential for continued evolution.

Figure 4: Breach log data showing use of curl and wget tools to connect to IP addresses associated with Outlaw.

Outlaw in 2023

Given Outlaw’s widespread notoriety and its continued activities, it is likely to remain a prominent threat to organizations and security teams across the threat landscape in 2023 and beyond.

As Darktrace has observed within its customer base from late 2022 through early 2023, activity linked with the Outlaw cryptocurrency mining campaign continues to transpire, offering security teams and research a renewed look at how it has evolved and adapted over the years. While many of its features and tactics appear to have remained consistent, Darktrace has identified numerous signs of Outlaw deviating from its previously known activities.

While relying on previously established IoCs and known tactics from previous campaigns will go some way to protecting an organization’s network from Outlaw compromises, there is a greater need than ever to go further than this. Rather than depending on a list of known-bads or traditional signatures and rules, Darktrace’s anomaly-based approach to threat detection and unparallel autonomous response capabilities mean it is uniquely positioned to DETECT and RESPOND to Outlaw activity, regardless of how it evolves in the future.

Credit to: Adam Potter, Cyber Analyst, Nahisha Nobregas, SOC Analyst, and Ryan Traill, Threat Content Lead

Relevant DETECT Model Breaches:

Compliance / Incoming SSH  

Device / New User Agent and New IP

Device / New User Agent  

Anomalous Connection / New User Agent to IP Without Hostname  

Compromise / Crypto Currency Mining Activity  

Anomalous File / Internet Facing System File Download  

Anomalous Server Activity / New User Agent from Internet Facing System  

Anomalous File / Zip or Gzip from Rare External Location  

Anomalous File / Script from Rare External Location  

Anomalous Connection / Multiple Failed Connections to Rare Endpoint  

Compromise / Large Number of Suspicious Failed Connections  

Anomalous Server Activity / Outgoing from Server  

Compromise / Sustained TCP Beaconing Activity To Rare Endpoint

Indicators of Compromise

Indicator - Type - Description

/dota3.tar.gz​

File  URI​

Outlaw  payload​

/tddwrt7s.sh​

File  URI​

Outlaw  payload​

73e5dbafa25946ed636e68d1733281e63332441d​

SHA1  Hash​

Outlaw  payload​

debian-package[.]center​

Hostname​

Outlaw  C2 endpoint​

161.35.236[.]24​

IP  address​

Outlaw  C2 endpoint​

138.68.115[.]96​

IP  address​

Outlaw C2  endpoint​

67.205.134[.]224​

IP  address​

Outlaw C2  endpoint​

138.197.212[.]204​

IP  address​

Outlaw C2  endpoint​

45.9.148[.]59 ​

IP  address​

Possible  Outlaw C2 endpoint​

45.9.148[.]117​

IP  address​

Outlaw C2  endpoint​

45.9.148[.]125​

IP  address​

Outlaw C2  endpoint​

45.9.148[.]129​

IP  address​

Outlaw C2  endpoint​

45.9.148[.]99 ​

IP  address​

Outlaw C2  endpoint​

45.9.148[.]234​

IP  address​

Possible  Outlaw C2 endpoint​

45.9.148[.]236​

IP  address​

Possible  Outlaw C2 endpoint​

159.203.102[.]122​

IP  address​

Outlaw C2  endpoint​

159.203.85[.]196​

IP  address​

Outlaw C2  endpoint​

159.223.235[.]198​

IP  address​

Outlaw C2  endpoint​

MITRE ATT&CK Mapping

Tactic -Technique

Initial Access -T1190  Exploit - Public Facing Application

Command and Control - T1071 - Application - Layer Protocol

T1071.001 - Application Layer Protocol: Web Protocols

Impact - T1496 Resource Hijacking

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
Adam Potter
Senior Cyber Analyst

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August 10, 2026

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

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Key takeaways

  • Protecting enterprise AI requires defense in depth: Organizations should combine governance, identity and access controls, data security, secure development, runtime monitoring, behavioral detection, containment, and incident response rather than rely on a single control.
  • Agentic AI expands enterprise risk beyond the model: Autonomous agents interact with tools, APIs, identities, data, and other agents, creating identity, behavioral, structural, and accountability risks that can propagate across trusted systems.
  • An effective AI security stack spans multiple layers: Security teams should evaluate governance and visibility, identity and agent controls, data protection, secure MLOps and LLMOps, runtime security, and response and recovery capabilities.
  • Security controls should develop alongside enterprise AI adoption: Organizations should establish ownership, visibility, access controls, testing, monitoring, and recovery capabilities while using behavior-based detection and containment to address risks that static policies may not reveal.

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 is the most effective way to protect enterprise AI?

The most effective way to protect enterprise AI is through a defense-in-depth strategy that combines governance, identity and access controls, data security, secure AI development, runtime monitoring, behavioral detection, containment, and incident response. No single control can address AI risk across models, agents, data, infrastructure, and enterprise environments.

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

An enterprise AI security stack should span six integrated layers: governance and visibility; identity, access, and agent control; data security and privacy; secure MLOps and LLMOps; runtime security; and response and recovery. These layers should work together rather than as a linear maturity sequence, with runtime controls helping manage risk as governance evolves.

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.  

See how Darktrace / SECURE AI can strengthen the visibility and runtime security layers of your broader AI security program.

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August 11, 2026

Reimagining Your SOC: Overcoming Alert Fatigue with AI-Led Investigations  

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Key takeaways

  • AI-led investigation can help combat SOC alert fatigue by extending automation beyond detection into triage, contextual analysis, prioritization, and investigation, reducing repetitive analyst work while preserving human judgment for critical security decisions.
  • Alert fatigue becomes an operational risk when high alert volumes, fragmented context, manual investigation, and limited analyst capacity prevent SOC teams from efficiently assessing potentially meaningful security activity.
  • Effective alert fatigue solutions should combine automated triage, contextual analysis, intelligent prioritization, and explainable investigation workflows so analysts can understand why activity matters and focus attention on higher-priority incidents.
  • AI-led investigation can improve SOC efficiency by expanding investigation coverage and freeing analysts from repetitive evidence gathering, creating more capacity for threat hunting, recovery activities, strategic decisions, and proactive cyber risk reduction.

The efficiency of a Security Operations Center (SOC) hinges on its ability to detect, analyze and respond to threats effectively. With advancements in AI and automation, key early SOC team metrics such as Mean Time to Detect (MTTD) have seen significant improvements:

  • 96% of defenders believing AI-powered solutions significantly boost the speed and efficiency of prevention, detection, response, and recovery.
  • Organizations leveraging AI and automation can shorten their breach lifecycle by an average of 108 days compared to those without these technologies.

While tool advances have improved performance and effectiveness in the detection phase, this has not been as beneficial to the next step of the process where initial alerts are investigated further to determine their relevance and how they relate to other activities. This is often measured with the metric Mean Time to Analysis (MTTA), although some SOC teams operate a two-level process with teams for initial triage to filter out more obviously uninteresting alerts and for more detailed analysis of the remainder. SOC teams continue to grapple with alert fatigue, overwhelmed analysts, and inefficient triage processes, preventing them from achieving the operational efficiency necessary for a high-performing SOC.

Addressing this core inefficiency requires extending AI's capabilities beyond detection to streamline and optimize the following investigative workflows that underpin effective analysis.

Challenges with SOC alert investigation

Detecting cyber threats is only the beginning of a much broader challenge of SOC efficiency. The real bottleneck often lies in the investigation process.

Detection tools and techniques have evolved significantly with the use of machine learning methods, improving early threat detection. However, after a detection pops up, human analysts still typically step in to evaluate the alert, gather context, and determine whether it’s a true threat or a false alarm and why. If it is a threat, further investigation must be performed to understand the full scope of what may be a much larger problem. This phase, measured by the mean time to analysis, is critical for swift incident response.

Challenges with manual alert investigation:

  • Too many alerts
  • Alerts lack context
  • Cognitive load sits with analysts
  • Insufficient talent in the industry
  • Fierce competition for experienced analysts

For many organizations, investigation is where the struggle of efficiency intensifies. Analysts face overwhelming volumes of alerts, a lack of consolidated context, and the mental strain of juggling multiple systems. With a worldwide shortage of 4 million experienced level two and three SOC analysts, the cognitive burden placed on teams is immense, often leading to alert fatigue and missed threats.

Even with advanced systems in place not all potential detections are investigated. In many cases, only a quarter of initial alerts are triaged (or analyzed). However, the issue runs deeper. Triaging occurs after detection engineering and alert tuning, which often disable many alerts that could potentially reveal true threats but are not accurate enough to justify the time and effort of the security team. This means some potential threats slip through unnoticed.

What are the key stages of detecting and stopping a cyber-attack?

Detecting and stopping a cyber incident involves several connected stages: identifying a trace of suspicious activity, generating a detection, investigating the alert to determine its context and scope, and containing confirmed threats. Each stage affects how quickly a SOC can move from identifying potentially malicious activity to taking effective action.

First we need a trace of an attack…

The attack will produce some sort of digital trace. Novel attacks, insider threats, and attacker techniques such as living-off-the-land can make attacker activities extremely hard to distinguish.

A detection is created…

Then we have to detect the trace, for example some beaconing to a rare domain. Initial detection alerts being raised underpin the MTTD (mean time to detection). Reducing this initial unseen duration is where we have seen significant improvement with modern threat detection tools.

When it comes to threat detection, the possibilities are vast. Your initial lead could come from anything: an alert about unusual network activity, a potential known malware detection, or an odd email. Once that lead comes in, it’s up to your security team to investigate further and determine if this is this a legitimate threat or a false alarm and what the context is behind the alert.

Investigation begins…

It doesn’t just stop at a detection. Typically, humans also need to look at the alert, investigate, understand, analyze, and conclude whether this is a genuine threat that needs a response. We normally measure this as MTTA (mean time to analyze).

Conducting the investigation effectively requires a high degree of skill and efficiency, as every second counts in mitigating potential damage. Security teams must analyze the available data, correlate it across multiple sources, and piece together the timeline of events to understand the full scope of the incident. This process involves navigating through vast amounts of information, identifying patterns, and discerning relevant details. All while managing the pressure of minimizing downtime and preventing further escalation.

Containment begins…

Once we confirm something as a threat, and the human team determines a response is required and understand the scope, we need to contain the incident. That's normally the MTTC (mean time to containment) and can be further split into immediate and more permanent measures.

For more about how AI-led solutions can help in the containment stage read here: Autonomous Response: Streamlining Cybersecurity and Business Operations

The challenge is not only in 1) detecting threats quickly, but also 2) triaging and investigating them rapidly and with precision, and 3) prioritizing the most critical findings to avoid missed opportunities. Effective investigation demands a combination of advanced tools, robust workflows, and the expertise to interpret and act on the insights they generate. Without these, organizations risk delaying critical containment and response efforts, leaving them vulnerable to greater impacts.

While there are further steps (remediation, and of course complete recovery) here we will focus on investigation.

Developing an AI analyst: How Darktrace replicates human investigation

Darktrace has been working on understanding the investigative process of a skilled analyst since 2017. By conducting internal research between Darktrace expert SOC analysts and machine learning engineers, we developed a formalized understanding of investigative processes. This understanding formed the basis of a multi-layered AI system that systematically investigates data, taking advantage of the speed and breadth afforded by machine systems.

With this research we found that the investigative process often revolves around iterating three key steps: hypothesis creation, data collection, and results evaluation.

All these details are crucial for an analyst to determine the nature of a potential threat. Similarly, they are integral components of our Cyber AI Analyst which is an integral component across our product suite. In doing so, Darktrace has been able to replicate the human-driven approach to investigating alerts using machine learning speed and scale.

Here’s how it works:

  • When an initial or third-party alert is triggered, the Cyber AI Analyst initiates a forensic investigation by building multiple hypotheses and gathering relevant data to confirm or refute the nature of suspicious activity, iterating as necessary, and continuously refining the original hypothesis as new data emerges throughout the investigation.
  • Using a combination of machine learning including supervised and unsupervised methods, NLP and graph theory to assess activity, this investigation engine conducts a deep analysis with incidents raised to the human team only when the behavior is deemed sufficiently concerning.
  • After classification, the incident information is organized and processed to generate the analysis summary, including the most important descriptive details, and priority classification, ensuring that critical alerts are prioritized for further action by the human-analyst team.
  • If the alert is deemed unimportant, the complete analysis process is made available to the human team so that they can see what investigation was performed and why this conclusion was drawn.
Darktrace cyber ai analyst workflow, how it works

To illustrate this via example, if a laptop is beaconing to a rare domain, the Cyber AI Analyst would create hypotheses including whether this could be command and control traffic, data exfiltration, or something else. The AI analyst then collects data, analyzes it, makes decisions, iterates, and ultimately raises a new high-level incident alert describing and detailing its findings for human analysts to review and follow up.

Learn more about Darktrace's Cyber AI Analyst

  • Cost savings: Equivalent to adding up to 30 full-time Level 2 analysts without increasing headcount
  • Minimize business risk: Takes on the busy work from human analysts and elevates a team’s overall decision making
  • Improve security outcomes: Identifies subtle, sophisticated threats through holistic investigations

How does AI-led investigation improve SOC efficiency?

AI-led investigation can improve SOC efficiency by shifting repetitive evidence gathering and triage away from analysts while preserving human judgment for higher-value decisions. The operational benefit is broader investigation coverage, faster prioritization, clearer reasoning, and more analyst time for activities such as threat hunting, recovery, and proactive cyber risk reduction.

Every relevant alert is investigated

The Cyber AI Analyst is not a generative AI system, or an XDR or SEIM aggregator that simply prompts you on what to do next. It uses a multi-layered combination of many different specialized AI methods to investigate every relevant alert from across your enterprise, native, 3rd party, and manual triggers, operating at machine speed and scale. This also positively affects detection engineering and alert tuning, because it does not suffer from fatigue when presented with low accuracy but potentially valuable alerts.

Retain and improve analyst skills

Transferring most analysis processes to AI systems can risk team skills if they don't maintain or build them and if the AI doesn't explain its process. This can reduce the ability to challenge or build on AI results and cause issues if the AI is unavailable. The Cyber AI Analyst, by revealing its investigation process, data gathering, and decisions, promotes and improves these skills. Its deep understanding of cyber incidents can be used for skill training and incident response practice by simulating incidents for security teams to handle.

Create time for cyber risk reduction

Human cybersecurity professionals excel in areas that require critical thinking, strategic planning, and nuanced decision-making. With alert fatigue minimized and investigations streamlined, your analysts can avoid the tedious data collection and analysis stages and instead focus on critical decision-making tasks such as implementing recovery actions and performing threat hunting.

Stay tuned for part 3/3

Part 3/3 in the Reimagine your SOC series explores the preventative security solutions market and effective risk management strategies.

Coming soon!

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About the author
Brittany Woodsmall
Product Marketing Manager, AI
Your data. Our AI.
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