AI-driven cybersecurity is rapidly expanding, with the global market estimated to reach $44.24 billion in 2026, from $34.09 billion in 2025. Predominantly cloud-centric, it enables the automation of threat detection, analysis and response, using intelligent, adaptive systems.
Per Research and Markets, the growth “stems from increased investment in AI-powered security across diverse sectors, as organisations adapt infrastructures and escalate cyber defence operations amid evolving threats and regulatory requirements”.
Traditional threat detection approaches rely heavily on human intervention and static, predefined rules. In contrast, fast-changing, data-heavy, scalable cloud environments are all about leveraging AI – machine learning security, using behaviour-based tools and virtual assistants and agents.
As such, cloud threat detection is becoming continuous, contextual and risk-based. According to research by Darktrace, defensive AI-driven cybersecurity is expected to impact “improving detection of new or unknown threats” the most (56.9%), followed by “improving threat detection in general” (55.3%), and “autonomously responding to threats” (43.0%).
Why must threat detection evolve in the cloud era?
In recent years, organisations have invested heavily in the public cloud due to its advanced capabilities and extensive service catalogue. The explosion of AI – mostly available from cloud providers – is accelerating adoption.
However, businesses aren’t moving all their services to the cloud – some remain on-premise in private data centres – meaning workloads need to run effectively across various platforms and environments.
But traditional security tools don’t work across the board, particularly in dynamic cloud settings. Alongside that, cyberattacks are happening faster, taking between 48 minutes and four hours to access and exfiltrate data in 2024, per ReliaQuest’s Cyber-Threat Report, versus 12-24 hours in previous years. For those reasons, companies need a more proactive approach towards cybersecurity.
From signatures to behaviour: history of threat detection
Before exploring the role of AI, some background. Traditional cybersecurity tools leverage signature-based detection rules, with threats triggered by a specific activity or event – for example, logging in with the wrong username and/or password.
The tools are primarily reactive, constantly monitoring thousands of predefined signatures or known malicious components – detecting and in some cases blocking activities that match. But if there’s a different type of attack using a new vector, the organisation won’t have a corresponding signature, meaning zero visibility.
The solution? Investing in new mechanisms to monitor abnormal activity in cloud settings. These go beyond single-action rules to encompass new and evolving attack patterns and vectors. With that in mind, focus has shifted towards detecting behaviour.
Behaviour-based tools are powered by machine learning models. A critical component of proactive cybersecurity, they gain an understanding of normal activity for employees, users and network traffic patterns, with anything unusual triggering a response.
Evolution of threat detection
With companies migrating workloads to the cloud and many embedding cloud-native security from the outset, behaviour-based tools and machine learning security are increasingly essential.
Beyond exposing abnormal behaviour, traffic or activity, the next step is how to act against threats, be it by blocking certain users or changing network rules. Security, orchestration, automation and response (SOAR) comes into play here, streamlining and accelerating detection and remediation.
Another key topic is the shift from perimeter-based security to zero-trust, an identity-centric concept based on continuous evaluation and micro-segmentation. Many zero-trust tools integrate with existing security products, enhancing threat detection.
How does AI transform cloud threat detection?
Over the last 15 years, organisations’ IT has become increasingly complex, going beyond private data centres to incorporate AI, infrastructure and collaboration services, sometimes across multiple cloud providers.
These fragmented landscapes are harder to monitor, because businesses can’t use the same signature or analytics rules internally as externally. In turn, the volume of what needs to be tracked has risen dramatically.
As a result, cloud cyber risks are increasing. Indeed, the 2025 Thales Cloud Security Study revealed that “64% of all enterprises regard cloud security as a pressing security discipline” while “54% cited an increase in direct attacks to compromise infrastructure”.
Additionally, Gartner predicts that by 2027, “AI agents will reduce the time it takes to exploit account exposures by 50%,” making breaches faster, as well as more frequent and efficient.
Artificial intelligence, in the form of machine learning models, is revolutionising cloud threat detection, facilitating forward-thinking, predictive cybersecurity. Alongside the mentioned benefits of helping understand and identify normal/unusual patterns, the algorithms can correlate incidents across different activities into a single alert. In turn, that reduces fatigue and burnout, something 84% of related professionals experience.
This form of AI-driven cybersecurity can also:
- Detect compromised credentials and insider threats.
- Identify advanced persistent threats and zero-days.
- Secure dynamic cloud workloads and containers.
- Accelerate response times.
- Automate real-time mitigation.
- Provide holistic visibility across multi-cloud.
- Rise of generative AI assistants and agents
Complementing behaviour-based cybersecurity tools are generative AI (GenAI) assistants and agents – an increasingly essential component of security operations centre (SOC) automation and augmentation. Why? They create human-readable summaries of alert logs, including action taken – the “ultimate partnership” with analysts.
Machine learning models generate the raw data, and 24/7 generative AI assistants and agents perform the initial heavy lifting – incident triage and investigation – saving valuable time. As part of SOC automation and augmentation, they also help:
- Mitigate alert fatigue (AI agent, often with assistant features)
- Democratise expertise (AI assistant)
- Operationalise cloud threat intelligence (AI agent, with guardrails)
Furthermore, if a new incident is detected with insufficient data, GenAI assistants and agents can automatically collect more information and add it to the summary. Humans then enter the equation, if necessary, to decide the best way forward.
Security analysts can also use AI-powered chatbots to deep dive into an incident, if there isn’t an artificial intelligence assistant attached to it – another time-saving tool.
AI-driven cybersecurity: risks and drawbacks
That being said, AI-driven cybersecurity raises various issues. First, the sheer amount of data required to understand what normal behaviour is, and therefore predict what isn’t (and eliminate false positives).
Meanwhile, if artificial intelligence agents take action – for example, changing a firewall or security product – do the relevant teams understand why? Do they know what data the agent was trained on, particularly if third parties are involved, raising ethical and risk questions?
At the same time, the technology makes it easier for hackers to carry out cyberattacks, because understanding coding is no longer a prerequisite. Indeed, AI is being used by a new wave of criminals with fewer IT skills to infiltrate organisations and swindle individuals.
Despite these considerations, AI and machine learning security have become critical for cloud threat detection, making the job of related professionals easier and evolving their roles.
Transforming cloud threat detection with AI-driven cybersecurity
With services fragmented across platforms and cyberattacks more advanced, threat detection models must evolve, using machine learning to identify behaviour and virtual assistants and agents to handle summarisations, automate remedial tasks and reduce investigation time. Meanwhile, chatbots make it easier for cybersecurity personnel to obtain information quickly. Artificial intelligence is integral to it all.
That being said, AI-driven cybersecurity isn’t about replacing human expertise, but augmenting it, enabling teams to focus on strategy, high-value tasks, resilience and innovation.