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July 16, 2025

Introducing the AI Maturity Model for Cybersecurity

The AI Maturity Model for Cybersecurity is the most detailed guide of its kind, grounded in real use cases and expert insight. It empowers CISOs to make strategic decisions, not just about what AI to adopt, but how to do it in a way that strengthens their organization over time and achieves successful outcomes.
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
Ashanka Iddya
Senior Director, Product Marketing
AI maturity model for cybersecurityDefault blog image
16
Jul 2025

AI adoption in cybersecurity: Beyond the hype

Security operations today face a paradox. On one hand, artificial intelligence (AI) promises sweeping transformation from automating routine tasks to augmenting threat detection and response. On the other hand, security leaders are under immense pressure to separate meaningful innovation from vendor hype.

To help CISOs and security teams navigate this landscape, we’ve developed the most in-depth and actionable AI Maturity Model in the industry. Built in collaboration with AI and cybersecurity experts, this framework provides a structured path to understanding, measuring, and advancing AI adoption across the security lifecycle.

Overview of AI maturity levels in cybersecurity

Why a maturity model? And why now?

In our conversations and research with security leaders, a recurring theme has emerged:

There’s no shortage of AI solutions, but there is a shortage of clarity and understanding of AI uses cases.

In fact, Gartner estimates that “by 2027, over 40% of Agentic AI projects will be canceled due to escalating costs, unclear business value, or inadequate risk controls. Teams are experimenting, but many aren’t seeing meaningful outcomes. The need for a standardized way to evaluate progress and make informed investments has never been greater.

That’s why we created the AI Security Maturity Model, a strategic framework that:

  • Defines five clear levels of AI maturity, from manual processes (L0) to full AI Delegation (L4)
  • Delineating the outcomes derived between Agentic GenAI and Specialized AI Agent Systems
  • Applies across core functions such as risk management, threat detection, alert triage, and incident response
  • Links AI maturity to real-world outcomes like reduced risk, improved efficiency, and scalable operations

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How is maturity assessed in this model?

The AI Maturity Model for Cybersecurity is grounded in operational insights from nearly 10,000 global deployments of Darktrace's Self-Learning AI and Cyber AI Analyst. Rather than relying on abstract theory or vendor benchmarks, the model reflects what security teams are actually doing, where AI is being adopted, how it's being used, and what outcomes it’s delivering.

This real-world foundation allows the model to offer a practical, experience-based view of AI maturity. It helps teams assess their current state and identify realistic next steps based on how organizations like theirs are evolving.

Why Darktrace?

AI has been central to Darktrace’s mission since its inception in 2013, not just as a feature, but the foundation. With over a decade of experience building and deploying AI in real-world security environments, we’ve learned where it works, where it doesn’t, and how to get the most value from it.

We've learned that modern businesses operate within a vast, interconnected ecosystem which introduces new complexities and vulnerabilities that make traditional cybersecurity approaches unsustainable. While many vendors use machine learning, not all AI tools are the same, and not all are created equal.

Darktrace’s Self-Learning AI uses a multi-layered AI approach, learning your unique organization to deliver proactive and resilient defense against today’s sophisticated threats. By strategically integrating a diverse set of AI techniques, such as machine learning, deep learning, LLMs, and natural language processing, both sequentially and hierarchically, our multi-layered AI approach provides a robust defense mechanism that is unique to your organization and adapts to the evolving threat landscape.

This model reflects that insight, helping security leaders find the right path forward for their people, processes, and tools.

Security teams today are asking big, important questions:

  • What should we actually use AI for?
  • How are other teams using it — and what’s working?
  • What are vendors offering, and what’s just hype?
  • Will AI ever replace people in the SOC?

These questions are valid, and they’re not always easy to answer. That’s why we created this model: to help security leaders move past buzzwords and build a clear, realistic plan for applying AI across the SOC.

The structure: From experimentation to autonomy

The model outlines five levels of maturity :

L0 – Manual Operations: Processes are mostly manual with limited automation of some tasks.

L1 – Automation Rules: Manually maintained or externally-sourced automation rules and logic are used wherever possible.

L2 – AI Assistance: AI assists research but is not trusted to make good decisions. This includes GenAI agents requiring manual oversight for errors.

L3 – AI Collaboration: Specialized cybersecurity AI agent systems  with business technology context are trusted with specific tasks and decisions. GenAI has limited uses where errors are acceptable.

L4 – AI Delegation: Specialized AI agent systems with far wider business operations and impact context perform most cybersecurity tasks and decisions independently, with only high-level oversight needed.

Each level reflects a shift, not only in technology, but in people and processes. As AI matures, analysts evolve from executors to strategic overseers.

Strategic benefits for security leaders

The maturity model isn’t just about technology adoption it’s about aligning AI investments with measurable operational outcomes. Here’s what it enables:

SOC fatigue is real, and AI can help

Most teams still struggle with alert volume, investigation delays, and reactive processes. AI adoption is inconsistent and often siloed. When integrated well, AI can make a meaningful difference in making security teams more effective

GenAI is error prone, requiring strong human oversight

While there is a lot of hype around GenAI agentic systems, teams will need to account for inaccuracy and hallucination in Agentic GenAI systems.

AI’s real value lies in progression

The biggest gains don’t come from isolated use cases, but from integrating AI across the lifecycle, from preparation through detection to containment and recovery.

Trust and oversight are key initially but evolves in later levels

Early-stage adoption keeps humans fully in control. By L3 and L4, AI systems act independently within defined bounds, freeing humans for strategic oversight.

People’s roles shift meaningfully

As AI matures, analyst roles consolidate and elevate from labor intensive task execution to high-value decision-making, focusing on critical, high business impact activities, improving processes and AI governance.

Outcome, not hype, defines maturity

AI maturity isn’t about tech presence, it’s about measurable impact on risk reduction, response time, and operational resilience.

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Outcomes across the AI Security Maturity Model

The Security Organization experiences an evolution of cybersecurity outcomes as teams progress from manual operations to AI delegation. Each level represents a step-change in efficiency, accuracy, and strategic value.

L0 – Manual Operations

At this stage, analysts manually handle triage, investigation, patching, and reporting manually using basic, non-automated tools. The result is reactive, labor-intensive operations where most alerts go uninvestigated and risk management remains inconsistent.

L1 – Automation Rules

At this stage, analysts manage rule-based automation tools like SOAR and XDR, which offer some efficiency gains but still require constant tuning. Operations remain constrained by human bandwidth and predefined workflows.

L2 – AI Assistance

At this stage, AI assists with research, summarization, and triage, reducing analyst workload but requiring close oversight due to potential errors. Detection improves, but trust in autonomous decision-making remains limited.

L3 – AI Collaboration

At this stage, AI performs full investigations and recommends actions, while analysts focus on high-risk decisions and refining detection strategies. Purpose-built agentic AI systems with business context are trusted with specific tasks, improving precision and prioritization.

L4 – AI Delegation

At this stage, Specialized AI Agent Systems performs most security tasks independently at machine speed, while human teams provide high-level strategic oversight. This means the highest time and effort commitment activities by the human security team is focused on proactive activities while AI handles routine cybersecurity tasks

Specialized AI Agent Systems operate with deep business context including impact context to drive fast, effective decisions.

Join the webinar

Get a look at the minds shaping this model by joining our upcoming webinar using this link. We’ll walk through real use cases, share lessons learned from the field, and show how security teams are navigating the path to operational AI safely, strategically, and successfully.

Find your place in the AI maturity model

Get the self-guided assessment designed to help you benchmark your current maturity level, identify key gaps, and prioritize next steps.

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
Ashanka Iddya
Senior Director, Product Marketing

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June 9, 2026

Healthcare’s OT Cybersecurity Gap: Why Hospitals Must Make the Same Security Investments as Regulated Critical Infrastructures

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Rethinking the healthcare attack surface

When most people think about Operational Technology (OT) cybersecurity, they think about oil & gas pipelines, utilities, manufacturing plants, or power grids. However, hospitals & healthcare systems have quickly become a point of focus in the OT cybersecurity community as they do employ a variety of OT in the form of IoMT (Internet of Medical Things) networked devices such as: infusion pumps, imaging systems, patient monitoring equipment, laboratory systems, and traditional industrial control systems (ICS) in the form of smart building management systems (BMS) and even on site power generation control systems. 

These healthcare environments are no longer just traditional IT ecosystems, they are cyber-physical environments where disruption can directly impact patient care, operational continuity, and ultimately patient safety.

The OT cybersecurity expertise gap in healthcare organizations

Our research in the OT cybersecurity space revealed a concerning trend. Many hospitals and healthcare networks lack dedicated OT cybersecurity teams, OT security full time employees (FTE) and even OT expertise in the form of OT security certifications when compared to other critical infrastructure sectors.

On the other hand, within industries such as energy and manufacturing, we encounter more mature OT security programs that employ full time employees  dedicated to OT cybersecurity with OT security certifications and expertise to secure industrial and operational environments and lead investment in OT security processes and technology.

When reviewing the top 20 U.S. Hospitals by market cap, given what is publicly available on LinkedIn, only one FTE with an OT cybersecurity certification was found. The certifications that were searched for include: GIAC GICSP, GIAC GRID, GIAC GCIP and all ISA/IEC 62443 certifications. When replicating this same search across the top 20 utility providers in the US, 73 FTEs with OT related certifications were identified. As a control group, we looked within financial services, an industry NOT expected to have OT systems worth investing in FTEs to protect. However, the top 20 US financial institutions had 18 FTEs with OT related certifications. 

What these findings reveal

Overall, the findings regarding healthcare investment in OT security FTEs are surprising given how operationally dependent modern healthcare has become on OT. So why aren't hospitals investing in OT security personnel at the rate of peer critical infrastructures? It could just be lack of awareness; however, there are other, more plausible reasons.  

Based on historical trends in cyber incidents within the healthcare space, one could speculate that there is significantly greater likelihood of being victim to an attack that  focuses on extortion or data theft rather than an attack on specific OT systems. The amount of ransomware events incurred in healthcare, that historically do not target OT systems, may divert attention and security investment to the parts of the attack surface most likely to be targeted by ransomware. Additionally, data theft is a relevant threat objective for hospitals given PHI, PCI and PII, and data theft does not traditionally align with attacks targeting OT.  

However, with focused investment to address data theft and with adversaries new capability to string together chains of vulnerabilities of different severity scores using advancements in AI, we could be entering a threat landscape where adversaries pivot their tactics to target exposed and under protected devices and systems like OT. For example, although not a patient records database, predominant IOMT protocols HL7 and DICOM are unencrypted plaintext protocols and unless encrypted it is very simple for adversaries, who are sniffing traffic, to identify protected health information (PHI) in these communication protocols.

Why OT cybersecurity expertise can be effective for healthcare organizations

The convergence of IT, OT, and IoMT is already here, and threat actors are increasingly aware of the operational vulnerabilities that come with it. Additionally, as AI solutions such as agentic or generative applications are adopted and deployed, the attack surface will continue to change as permissions, and new connections will exist to support AI efficiency. From a cybersecurity standpoint, the reality is that many healthcare organizations are still working to establish consistent visibility and governance across their enterprise-connected devices and systems as their attack surface is changing in real time.  As the healthcare sector remains a significant target for cyber-attacks, hospitals would be well advised to begin addressing their operational environments OT as a critical component of their attack surface and invest in securing them first with people, then process and technology. 

What can healthcare organizations do to secure their OT

Including OT in current cybersecurity processes such as red teaming and testing incident response plans that take OT into account alongside building dedicated OT security capabilities including improving OT network visibility, leveraging OT network anomaly detection, micro-segmentation, and secure remote access will become essential steps in strengthening healthcare resilience. 

However, before any of the above processes or investments in technology can be made, these healthcare organizations, like the other critical infrastructure sectors, need to invest in the people with the experience in OT security to lead, implement, manage and audit the investment in OT cybersecurity technology and processes.  In cases where headcount cannot be added, investment in OT security certifications, such as the ones listed in this article, and participation on OT security events focused on practitioner training for existing cybersecurity employees can move the needle in terms of bringing OT expertise to the existing team.  

In an industry where uptime and safety are as mission critical as they are for a power utility, OT cybersecurity FTEs can no longer be viewed as optional for healthcare organizations and must become part of the foundation of modern healthcare cybersecurity strategy. 

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About the author
Daniel Simonds
Director of Operational Technology

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June 9, 2026

Always On, Always Defending: Inside the AI-Driven SOC

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Today’s SOC: A system under pressure

The SOC has been described as the:

  • Control center for security systems management  
  • Operations center for log analysis and alert response
  • Command center for network monitoring and investigation

But the CISO at a manufacturer of industrial power solutions says today’s SOC is far more dynamic:

“The SOC is an active player in a never-ending chess match where the pieces are always moving, the rules are constantly changing, and we’re continuously adjusting our tactical and strategic approaches to keep up.”

This has created a balancing act for cybersecurity professionals:

  • Support expanding digital estates to fuel innovation…or risk limiting business growth
  • Stop advanced cyberattacks at scale…or risk severe financial and reputational impacts

But balancing these responsibilities is increasingly difficult. Attackers are operating at machine speed and scale using sophisticated, adaptive techniques that overwhelm teams and bypass legacy defenses. At the same time, more than half of cybersecurity teams are understaffed, and 65% have unfilled cybersecurity positions (ISACA).

“The SOC is hitting its breaking point,” admits the VP of IT at a U.S.-based risk management services provider.”

“That’s the hard reality,” affirms a Chief Digital and Technology Officer at a North American financial services organization. “SOC teams are drowning in alerts, wasting time researching the most benign incidents while missing critical threats.”

Traditional tools lack the context and autonomous reasoning needed to determine which ones are truly dangerous, requiring analysts to manually review and respond. But with thousands of alerts hitting SOCs daily, the task exceeds human capacity, with recent industry research revealing that 40% to 42% of security alerts now go uninvestigated.

“Our old governance models of throwing bodies at it, that’s not going to work,” says the Group CIO of a multinational holding company. “Attackers move at machine speed, and our defenses have to operate at the same pace. Using AI for cybersecurity is the only way to do that.”

Why AI is essential

AI is about speed, scale, and context.

SOC teams are still expected to find the proverbial “needle in a haystack”, but the haystack keeps growing. As digital infrastructures expand and threat actors use AI to rapidly scale attacks and exploit vulnerabilities, success isn’t about keeping up but changing the approach.

This is where AI comes in, enabling security teams to operate at machine speed and scale by:

  • Analyzing vast amounts of data and correlating signals across domains within seconds
  • Detecting possible threats in real time and taking immediate action to mitigate risk
  • Prioritizing threats by severity and uncovering contextual details for rapid triage

The power of AI isn’t theoretical; it is transforming how today’s businesses operate.

The Chief Digital and Technology Officer at a financial services firm says within a single month of using Darktrace, the solution tracked billions of network events, autonomously investigated tens of millions of those incidents, and added the equivalent of 1,000 analyst hours of investigation. It also found threats that bypassed traditional tools, autonomously responding to contain or disrupt the threat on over 30,000 emails, including 18,000 the firm’s native email filter missed.

When Darktrace says it “takes action on a threat,” it generally means its platform can move beyond just detecting suspicious activity and automatically respond to contain or disrupt the threat—such as isolating a device, slowing or blocking suspicious network traffic, disabling risky user activity, or triggering security workflows—depending on how the system is configured.

AI isn’t about displacing humans.

AI is a powerful tool for handling large-scale data analysis, pattern detection, and repetitive tasks, but it cannot replace human critical thinking. By removing mindless work that does not require judgment, AI frees analysts to focus on what humans do best: applying reasoning, context, and sound decision-making to complex threats.

“AI is a workforce maximizer,” says the Chief Digital and Technology Officer. “It augments our team by monitoring and detecting threats at a scale beyond human capacity while providing the critical context we need to make faster, more confident decisions.

Rather than replacing people, AI is changing how security professionals work. Analysts can reclaim time previously spent on tedious, manual triage to focus on higher priorities and proactive initiatives like advanced threat hunting, strategic risk management, and security enablement and training.

“Aside from risk mitigation, our biggest ROI is in efficiency,” says the Head of Security at global business services provider. “What used to take 90% of our investigation time is now handled automatically, so we can focus on the final 10%, which requires critical thinking."

For SOC teams under pressure, the impact can be transformative, with security leaders reporting significant real-world outcomes using Darktrace Self-Learning AITM, including:

  • Phishing emails reduced by 99%
  • 1 million+ emails autonomously analyzed each month, with no email-based incidents reported
  • Potential threats autonomously neutralized in under four seconds, on average  
  • 99% of investigations conducted autonomously, surfacing only the high-priority 1% of threats for analyst review

How AI optimizes the SOC

To protect the modern enterprise, you absolutely need the right tools,” says CTO at leading European fashion brand. “Without them you’re a victim. With them, you’re a defender. AI and the machine speed detect/response it enables makes it the most critical tool.”

Replacing chaos with clarity and control  

It’s important to note that different AI solutions address different needs. Companies should clearly understand their specific use case and select the solution that best aligns with their goals, requirements, and operational needs.  

When it comes to choosing cybersecurity in a machine-speed threat landscape, time is the most valuable resource. Organizations require AI that can move from insight to action by:

  • Learning an organization’s unique behavioral patterners
  • Correlating signals across domains to detect anomalous activity
  • Prioritizing events and autonomously responding at scale to the vast majority
  • Quarantining high-impact threats until the SOC can investigate
  • Arming analysts with deep, contextual information to accelerate investigations

“Darktrace AI gives us threat detections based on facts, not guesses,” says the Group CIO. “It moves the SOC beyond alert overload to confident, informed decision-making. When Darktrace flags something, we pay attention. False positives are very rare, so we act with speed and confidence without second-guessing.”

Replacing anxiety with confidence and peace of mind

Every missed alert can have real-world consequences.

The strain of maintaining constant vigilance at scale without holistic visibility and automation is taking its toll on security professionals: 66% report increased stress, and nearly half say it’s the reason they’re leaving the field (ISACA).

The CIO at a professional sports organization says that’s not surprising: “If you don’t know what’s going on, anything could be happening. Operating with that level of uncertainty and control is incredibly stressful.”

AI gives SOCs the power to be proactive by unifying telemetry across network, email, identity, and cloud environments to provide a complete picture and a stronger foundation for action. The benefits for analysts, both personally and professionally, are significant:

  • Achieve greater work-life balance: “Knowing that Darktrace has our backs 24/7 and will take immediate action to stop threats  means we can now work normal hours and take vacations without worrying,” says the Chief Digital and Technology Officer.
  • Feel in control with deeper insights: “It not only stops and quarantines threats but also provides the deep context we need to quickly investigate and respond,” explains the Head of Security.  
  • Gain confidence the business is protected 24/7: “We can sleep at night. With Darktrace I’m confident that even with a small team we can protect the business 24/7,” adds the former retail CIO.

The modern SOC: A system of balance

Elevated to a core pillar of business strategy, the modern SOC is now considered:

  • The nerve center of cyber risk and proactive defense
  • The AI-powered command center for operational resilience
  • The strategic hub for contextual decision-making at scale

The SOC has evolved from a reactive center responsible for managing systems into a proactive, frontline defender and strategic business enabler—integral to innovation and growth.

AI is the key to balancing these responsibilities.

“We can only grow as fast as we can secure the business,” says the Head of Security. “AI gives us the speed, scale, and confidence to do both.”

*Metrics are based on the customer’s interview, data and sourced from its monthly Cyber AI Insights reporting.

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