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February 18, 2025

Unifying IT & OT With AI-Led Investigations for Industrial Security

Discover how AI-led investigations unify IT and OT security, reducing alert fatigue and accelerating alert investigation in industrial environments.
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
Daniel Simonds
Director of Operational Technology
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18
Feb 2025

As industrial environments modernize, IT and OT networks are converging to improve efficiency, but this connectivity also creates new attack paths. Previously isolated OT systems are now linked to IT and cloud assets, making them more accessible to attackers.

While organizations have traditionally relied on air gaps, firewalls, data diodes, and access controls to separate IT and OT, these measures alone aren’t enough. Threat actors often infiltrate IT/Enterprise networks first then exploit segmentation, compromising credentials, or shared IT/OT systems to move laterally, escalate privileges, and ultimately enter the OT network.

To defend against these threats, organizations must first ensure they have complete visibility across IT and OT environments.

Visibility: The first piece of the puzzle

Visibility is the foundation of effective industrial cybersecurity, but it’s only the first step. Without visibility across both IT and OT, security teams risk missing key alerts that indicate a threat targeting OT at their earliest stages.

For Attacks targeting OT, early stage exploits often originate in IT environments, adversaries perform internal reconnaissance among other tactics and procedures but then laterally move into OT first affecting IT devices, servers and workstations within the OT network. If visibility is limited, these threats go undetected. To stay ahead of attackers, organizations need full-spectrum visibility that connects IT and OT security, ensuring no early warning signs are missed.

However, visibility alone isn’t enough. More visibility also means more alerts, this doesn’t just make it harder to separate real threats from routine activity, but bogs down analysts who have to investigate all these alerts to determine their criticality.

Investigations: The real bottleneck

While visibility is essential, it also introduces a new challenge: Alert fatigue. Without the right tools, analysts are often occupied investigating alerts with little to no context, forcing them to manually piece together information and determine if an attack is unfolding. This slows response times and increases the risk of missing critical threats.

Figure 1: Example ICS attack scenario

With siloed visibility across IT and OT each of these events shown above would be individually alerted by a detection engine with little to no context nor correlation. Thus, an analyst would have to try to piece together these events manually. Traditional security tools struggle to keep pace with the sophistication of these threats, resulting in an alarming statistic: less than 10% of alerts are thoroughly vetted, leaving organizations vulnerable to undetected breaches. As a result, incidents inevitably follow.

Darktrace’s Cyber AI Analyst uses AI-led investigations to improve workflows for analysts by automatically correlating alerts wherever they occur across both IT and OT. The multi-layered AI engine identifies high-priority incidents, and provides analysts with clear, actionable insights, reducing noise and highlighting meaningful threats. The AI significantly alleviates workloads, enabling teams to respond faster and more effectively before an attack escalates.

Overcoming organizational challenges across IT and OT

Beyond technical challenges like visibility and alert management, organizational dynamics further complicate IT-OT security efforts. Fundamental differences in priorities, workflows, and risk perspectives create challenges that can lead to misalignment between teams:

Non-transferable practices: IT professionals might assume that cybersecurity practices from IT environments can be directly applied to OT environments. This can lead to issues, as OT systems and workflows may not handle IT security processes as expected. It's crucial to recognize and respect the unique requirements and constraints of OT environments.

Segmented responsibilities: IT and OT teams often operate under separate organizational structures, each with distinct priorities, goals, and workflows. While IT focuses on data security, network integrity, and enterprise applications, OT prioritizes uptime, reliability, and physical processes.

Different risk perspectives: While IT teams focus on preventing cyber threats and regulatory violations, OT teams prioritize uptime and operational reliability making them drawn towards asset inventory tools that provide no threat detection capability.

Result: A combination of disparate and ineffective tools and misaligned teams can make any progress toward risk reduction at an organization seem impossible. The right tools should be able to both free up time for collaboration and prompt better communication between IT and OT teams where it is needed. However, different size operations structure their IT and OT teams differently which impacts the priorities for each team.

In real-world scenarios, small IT teams struggle to manage security across both IT and OT, while larger organizations with OT security teams face alert fatigue and numerous false positives slowing down investigations and hindering effective communication with the IT security teams.

By unifying visibility and investigations, Darktrace / OT helps organizations of all sizes detect threats earlier, streamline workflows, and enhance security across both IT and OT environments. The following examples illustrate how AI-driven investigations can transform security operations, improving detection, investigation, and response.

Before and after AI-led investigation

Before: Small manufacturing company

At a small manufacturing company, a 1-3 person IT team juggles everything from email security to network troubleshooting. An analyst might see unusual traffic through the firewall:

  • Unusual repeated outbound traffic from an IP within their OT network destined to an unidentifiable external IP.

With no dedicated OT security tools and limited visibility into the industrial network, they don’t know what the internal device in question is, if it is beaconing to a malicious external IP, and what it may be doing to other devices within the OT network. Without a centralized dashboard, they must manually check logs, ask operators about changes, and hunt for anomalies across different systems.

After a day of investigation, they concluded the traffic was not to be expected activity. They stop production within their smaller OT network, update their firewall rules and factory reset all OT devices and systems within the blast radius of the IP device in question.

After: Faster, automated response with Cyber AI Analyst

With Darktrace / OT and Cyber AI Analyst, the IT team moves from reactive, manual investigations to proactive, automated threat detection:

  • Cyber AI Analyst connects alerts across their IT and OT infrastructure temporally mapping them to attack frameworks and provides contextual analysis of how alerts are linked, revealing in real time attackers attempting lateral movement from IT to OT.
  • A human-readable incident report explains the full scope of the incident, eliminating hours of manual investigation.
  • The team is faster to triage as they are led directly to prioritized high criticality alerts, now capable of responding immediately instead of wasting valuable time hunting for answers.

By reducing noise, providing context, and automating investigations, Cyber AI Analyst transforms OT security, enabling small IT teams to detect, understand, and respond to threats—without deep OT cybersecurity expertise.

Before: Large critical infrastructure organization

In large critical infrastructure operations, OT and IT teams work in separate silos. The OT security team needs to quickly assess and prioritize alerts, but their system floods them with notifications:

  • Multiple new device connected to the ICS network alerts
  • Multiple failed logins to HMI detected
  • Multiple Unusual Modbus/TCP commands detected
  • Repeated outbound OT traffic to IT destinations

At first glance, these alerts seem important, but without context, it’s unclear whether they indicate a routine error, a misconfiguration, or an active cyber-attack. They might ask:

  • Are the failed logins just a mistake, or a brute-force attempt?
  • Is the outbound traffic part of a scheduled update, or data exfiltration?

Without correlation across events, the engineer must manually investigate each one—checking logs, cross-referencing network activity, and contacting operators—wasting valuable time. Meanwhile, if it’s a coordinated attack, the adversary may already be disrupting operations.

After: A new workflow with Cyber AI Analyst

With Cyber AI Analyst, the OT security team gets clear, automated correlation of security events, making investigations faster and more efficient:

  • Automated correlation of OT threats: Instead of isolated alerts, Cyber AI Analyst stitches together related events, providing a single, high-confidence incident report that highlights key details.
  • Faster time to meaning: The system connects anomalous behaviors (e.g., failed logins, unusual traffic from an HMI, and unauthorized PLC modifications) into a cohesive narrative, eliminating hours of manual log analysis.
  • Prioritized and actionable alerts: OT security receives clear, ranked incidents, immediately highlighting what matters most.
  • Rapid threat understanding: Security teams know within minutes whether an event is a misconfiguration or a cyber-attack, allowing for faster containment.

With Cyber AI Analyst, large organizations cut through alert noise, accelerate investigations, and detect threats faster—without disrupting OT operations.

An AI-led approach to industrial cybersecurity

Security vendors with a primary focus on IT may lack insight into OT threats. Even OT-focused vendors have limited visibility into IT device exploitation within OT networks, leading to failed ability to detect early indicators of compromise. A comprehensive solution must account for the unique characteristics of various OT environments.

In a world where industrial security is no longer just about protecting OT but securing the entire digital-physical ecosystem as it interacts with the OT network, Darktrace / OT is an AI-driven solution that unifies visibility across IT, IoT and OT, Cloud into one cohesive defense strategy.

Whether an attack originates from an external breach, an insider threat, a supply chain compromise, in the Cloud, OT, or IT domains Cyber AI Analyst ensures that security teams see the full picture - before disruption occurs.

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Learn more about Darktrace / OT 

Unify IT and OT security under a single platform, ensuring seamless communication and protection for all interconnected devices.

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
Daniel Simonds
Director of Operational Technology

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May 28, 2026

From Efficiency to Exposure: How AI Adoption Is Creating Unseen Vulnerabilities on the Factory Floor

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How AI agents impact the manufacturing industry

Security teams and IT personnel across the manufacturing industry are under constant pressure to protect production, maintain uptime, and safeguard critical assets but the rise of AI is bringing huge new opportunities alongside new cyber risks. Across manufacturing, AI is embedded into workflows, decision-making, and increasingly, autonomous AI agents are acting on behalf of employees and systems.  

Agentic systems are powerful because they can act independently, but that same autonomy also creates cyber and operational risk. Agents have extensive permissions and are capable of carrying out complex tasks, making decisions, and interacting with tools or external systems with little to no human intervention.

Unlike traditional AI models that perform predefined tasks, AI agents use advanced techniques to mimic human decision-making processes, dynamically adapting to new challenges, making decision and taking action based on their own judgement. They look like employees operationally but lack judgment, ethics, or fear of consequences like humans do. This means they can be easily manipulated by cybercriminals, and an AI agent embedded across an OT network creates threats that extend well beyond data exposure. For example, at BMW, AI identifies faults in welding processes as they occur. At its Spartanburg plant, AI monitors the weld of 300-400 metal studs onto every SUV frame to detect misplaced or faulty studs and correct them instantly. Corruption of BMW’s AI system could lead to catastrophic quality control errors.

Adopting agentic AI systems across manufacturing raises some concerns across security teams. New data from our State of AI Cybersecurity survey shows that 78% of manufacturing security professionals are worried about employee use of AI agents – their top concern. That’s followed by employee use of generative AI tools like CoPilot and ChatGPT, a worry for 76% of security professionals at manufacturing organizations. As these tools gain more access to business data and processes, and more autonomy within organizations, security teams, who today have minimal visibility of agent activity in their environments, increasingly have sensitive data exposure (a worry for 60%) and accidental policy and regulatory violations (59%) on their minds.

External AI-powered threats are evolving just as quickly

The same capabilities transforming manufacturing are also reshaping cyberattacks.

AI is enabling attackers to automate reconnaissance, refine targeting, and adapt in real time. What once required time and manual effort can now be executed continuously and at scale. Manufacturers are already seeing the impact. According to manufacturing security professionals we surveyed, 76% are already being impacted by AI-powered threats and 90% see AI increasing the success of social engineering attacks.

And the techniques themselves are evolving. Concerns across the manufacturing sector show growing anxiety about the range of AI-powered attack routes, most pressingly of adaptive malware that evolves in real-time – a prospect half (49%) of manufacturing security professionals we surveyed are worried by, a full 9% more than the average across industries. AI adaptive malware is followed by:

  • Automated vulnerability scanning and exploit chaining (48%) which has become even more pressing as Anthropic’s new Mythos AI Model supercharges vulnerability discovery
  • Hyper-personalized phishing campaigns (46%), which remain a mainstay in hackers’ arsenals, and AI has amplified their effectiveness by making phishing emails more convincing and harder to detect.

This is not just an increase in volume, it is a shift toward threats that evolve as they unfold - often faster than static defenses can respond.

Despite rising awareness, many manufacturers are not yet equipped to manage this shift. More than half (51%) say they are not adequately prepared for AI-driven threats, and only 37% have formal policies governing AI deployment.  

Securing AI through visibility, context, and guardrails

Addressing this challenge does not require manufacturers to slow innovation. It requires a different approach to security, one that can operate at the same speed and scale as AI. Three specific priorities are emerging for manufacturers looking to take advantage of the power of AI.

Visibility is foundational.  

Organizations need to understand where AI is being used, what it can access, and how it behaves across both IT and OT environments. Without that, risk cannot be measured or managed. It is no surprise that Darktrace’s research found that 91% of manufacturing security professionals said that they need to understand how AI makes decisions before trusting it. This is even more critical in operational settings where disruption has safety, environmental, financial, and reputational impacts.

Context is what turns visibility into action.  

In environments shaped by AI, normal behavior is constantly shifting. Detecting threats requires a behavioral approach; understanding patterns of life across the organization and identifying subtle deviations in real time – a step change in organizations’ traditional approach to security and risk management.

Guardrails ensure that agency does not become exposure  

As AI systems take on greater responsibility, organizations need clear boundaries around what they can do and when they can act independently. These controls must be embedded into systems themselves, not applied after the fact.  

Securing AI Agents Across Manufacturing IT and OT

The rise of agentic AI is transforming manufacturing - powering next-generation operations while reshaping the security landscape. This is not just an increase in threats, but a shift to autonomous systems, continuously evolving behaviors, and risks moving at machine speed. For organizations trying to grapple with the challenge of enabling AI while managing the risk, visibility, context and guardrails should be foundational.

Darktrace helps manufacturers build secure AI approaches by making those foundations possible. It provides visibility and real-time detection and response to unusual activity across IT and OT environments and allows organizations to understand AI activity from the prompts employees use and the agents they build to how those agents are behaving across the environment. For manufacturers scaling AI, this delivers a foundation for innovation without sacrificing control.

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About the author
Oakley Cox
Director of Product

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May 28, 2026

How to Evaluate AI Vendors: 5 Key categories for AI Adoption

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Understanding the AI buyers’ market

AI adoption has become a central topic of discussion in boardrooms, drawing growing interest from business leaders. Ultimately, organizations hope that an investment in AI technology will have tremendous returns. However, the process of buying an AI solution is not as straight forward as it appears on the surface.  

While business leaders may be eager to improve productivity across their operations, practitioners responsible for evaluating and selecting AI solutions may not always have the visibility or technical understanding needed to make the right decisions for their business. What is typically marketed as a holistic solution to their most critical problems is usually followed by uncertainty when AI tools are finally operationalized in real environments.

This guide is intended to support security leaders who are under growing pressure to adopt AI tools while navigating complex terminology, vendor claims, and increasingly crowded buying cycles. Ultimately, the goal is to help organizations evaluate and adopt AI in a safe, effective, and well-governed way. To support this, we’ve structured the evaluation framework across five key categories:

  1. Governance, safety, and data controls
  1. Data gathering and training
  1. Model and technique choice
  1. Performance and accuracy validation    
  1. Interpretability, adjustability, and transparency    

What buying AI looks like in cybersecurity

While investing in AI can bring immense benefits to your security team, first-time buyers of AI cybersecurity solutions may not know where to start. They will have to determine the type of tool they want, know the options available, and evaluate vendors. Research and understanding are critical to ensure purchases are worth the investment.  

With acceleration in AI adoption, accompanied by the recent boom in agentic AI and autonomous agents, CISOs must look “beneath the hood" of these tools to understand how they work, how they are governed, and to ensure the system is secure and compliant with internal policies.

Challenges in the AI buyers’ marketplace  

The AI security software market is buzzing with hype and flashy promises, which, understandably, needs to be addressed with due diligence. Potential buyers, especially in the cybersecurity space, are hesitant when it comes to allowing AI autonomous capabilities across their workflows, and a lack of vendor transparency can exacerbate those feelings.  

Reinforcing this sentiment, research from this year's Darktrace’s State of AI Cybersecurity report shows where confidence and hesitancy emerge amongst potential buyers. On the one hand, security professionals agree that they have good visibility into the logic and reasoning processes their AI solutions use. However, they lack the explainability and trust to allow AI to take independent remedial action.

  • 89% say they have good visibility into the reasoning behind the outputs generated by AI solutions
  • 92% say they need to understand how a defensive AI tool makes decisions before they can trust it
  • Only 14% say they allow AI to act independently, performing autonomous actions without human approval
  • 74% say they are limiting the autonomy of AI taking action in their SOC until explainability improves

Given the desire for trust and explainability we are seeing from buyers, it's important for them to be equipped with the right questions to ask vendors during an assessment or POV of AI tools in order to demystify marketing hype from real operational outcomes.

Below is a list of categories in which buyers can assess AI vendors or AI Service Providers (AISPs) to help reach safe adoption and maximize their ROI.  

5 categories of AI vendor assessment

Darktrace groups these AI-related questions into 5 categories: governance, data and training, model and technique choice, performance validation, and interpretability and adjustability. By asking questions regarding each of these 5 categories, buyers can gain a deeper understanding of how an AISP’s systems work and whether they suit their business requirements.

Governance, safety, and data controls

Governance of AI systems is critical for all AISPs. Whether their platform is based around a single model, or is a more complex, composite AI solution, strong governance is essential to ensure the system is safe, robust, and reliable.

A simple question you could ask is:

What AI governance policies and frameworks do you follow, and/or certifications do you currently maintain?

For more questions you can ask vendors, download the full guide here.

Darktrace is certified to the ISO/IEC 42001 standard, the world’s first AI Management System (AIMS) standard. ISO/IEC 42001 addresses the unique ethical and technical challenges AI poses by setting out a structured way to manage risks such as transparency, accuracy, and misuse. This includes a commitment to ethical AI development, and effective management and monitoring of AI systems both prior to and continually after release.

Data gathering and training

Accurate, meaningful, and unbiased data gathering is the first important step in producing any AI system. An AI model trained using inaccurate, unbalanced, or poor-quality training data will fail to perform optimally.

To alleviate concerns regarding training data quality, a question you could ask is:

What steps do you take to prevent bias in your AI models and training data?

For more questions, download the full guide here.

AISPs should be able to provide information about the steps taken, workflows followed, and auditing performed to reduce AI bias where appropriate. While it’s sometimes impossible to fully remove bias from an AI model, appropriate actions should be taken to mitigate or reduce bias where relevant.

Model and technique choice

Different AI techniques are optimal for different tasks. For example, research from Gartner suggests that relying on a single “one-size-fits-all" model can lead to data gaps, especially in highly specialized domains.

To achieve more accurate and robust AI solutions, AI leaders should move beyond using just one model or technique, embrace composite AI practices, and adopt a holistic AI system perspective.

A straightforward question you could ask is simply:

What type(s) of AI model(s) do you utilize in your solution?

For more questions, download the full guide here.

While specific detailed information about custom systems used by AISPs is likely proprietary, buyers should expect vendors to be able to provide an overview of the broad techniques used. This will allow you as a buyer to determine if the type of model is appropriate for your use case.

Performance and accuracy validation  

Testing and evaluation of performance is essential for all AI systems. Performance analysis should be performed both before release and continually after release to identify potential data or model drift.  

A question you could ask to understand an AISPs testing workflow is:

How do you audit, test, evaluate, verify, and validate your AI model outputs?

For more questions, download the full guide here.

Testing workflows will likely vary depending on the type of model – measurements relevant to one system may not always be relevant to others. Assessment of systems should also extend beyond these standard accuracy and robustness tests, and should also feature physical performance, such as latency and resource consumption.  

Interpretability, adjustability, and transparency  

AI systems are typically a black box, simply providing an output without an explanation of how that output was attained. Interpretability and transparency are critical to ensure that both SOC teams and end-users trust the outputs of a system to be accurate and meaningful.

A question you could ask is:

How do you promote a trust relationship between human analysts and AI outputs?

For more questions, download the full guide here.

In the context of cybersecurity, trust and interpretability are even more essential. This is particularly relevant for generative AI-based systems (including most AI Agents), where the risk of hallucination can reduce trust in responses.

Cybersecurity systems often need to perform autonomous actions to block incoming threats – an email filtering system may hold potentially dangerous emails; a firewall may block malicious inbound connections. If SOC teams can’t trust these systems to perform accurately, these systems may be limited or disabled, critically reducing their defensive power.

Darktrace as an AI-native cybersecurity vendor

Darktrace has been building and applying AI in cybersecurity for over a decade, developing its capabilities alongside an increasingly complex and fast‑moving threat landscape. This experience has resulted in a mature, multi-layered approach to AI, which continuously learns the normal patterns of each organization to understand behavior, interpret context, and identify meaningful deviations — without relying on predefined rules or known attack signatures. Over time, this has enabled a proven behavioral understanding that helps uncover subtle signals of risk that may otherwise be missed.

With the backing of our ISO/IEC 42001 certification, stakeholders, customers, and partners can be confident that Darktrace is responsibly, ethically, and safely developing its AI systems, and managing the use of AI in day-to-day operations in a compliant and secure manner.  

Explore the principles behind Darktrace’s responsible AI approach, informed by collaboration with global experts in academia and governments, detailing how accountability, explainability, and continuous validation are built into its cybersecurity technology.

How Darktrace secures AI systems

Darktrace now brings these capabilities to monitor and respond to risk generated from AI systems across organizations with Darktrace / SECURE AI. This solution analyzes how prompts, agents, and systems are used within the context of each organization, bringing every AI interaction into a single view. This unique approach helps teams understand intent, assess risk, protect sensitive data, and enforce policy across both human and AI agent activity.

Stay up to date

Sign up for the Secure AI Readiness Program here: This gives you exclusive access to the latest news on the latest AI threats, updates on emerging approaches shaping AI security, and insights into the latest innovations, including Darktrace’s ongoing work in this area.

Ready to talk with a Darktrace expert on securing AI? Register here to receive practical guidance on the AI risks that matter most to your business, paired with clarity on where to focus first across governance, visibility, risk reduction, and long-term readiness.  

Further Reading on AI in cybersecurity

When deciding to invest in an AI solution, it’s important to understand what this means for you and your organization. The questions presented here are only a starting point in understanding an AI solution and whether it is appropriate for your use case.  

Gain deeper knowledge on applications of AI in cybersecurity and Darktrace’s multi-layered AI in the AI Arsenal White Paper.

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About the author
Jamie Bali
Technical Author (AI) Developer
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