Why preemptive, agentic, and zero-trust security will be embedded deep into PaaS by 2026

From detect-and-respond to preemptive defense

For more than a decade, enterprise security has revolved around one core rhythm: detect, investigate, respond. Security operations centers collected logs, triaged alerts, and rushed to contain threats that had already made it past the first lines of defense. That model is now under sustained pressure.

Attackers automate at scale, exploit zero-days within hours, and increasingly experiment with their own AI tools. Meanwhile, cloud complexity keeps growing. Multicloud and hybrid architectures scatter workloads across providers and regions, creating more entry points and more blind spots. In this environment, waiting for a detection rule to fire is often too late.

Gartner has put a name and a number to the shift that is underway. It forecasts that by 2030, preemptive cybersecurity technologies—tools designed to deny, deceive, and disrupt attackers before they achieve their goals—will account for around half of all IT security spending, up from less than 5 percent in 2024. Gartner+1 Standalone detection-and-response tools will not disappear, but they will increasingly be embedded into broader, proactive security platforms.

In parallel, AI is rapidly moving from experimental pilots into everyday security workflows. The Cloud Security Alliance’s State of AI and Security report shows that more than half of organizations plan to implement generative AI security solutions, while also voicing concern about new attack surfaces created by AI itself. Cloud Security Alliance+2Cloud Security Alliance+2 Put together, these trends point toward an architecture where AI-driven defense is deeply woven into the multicloud fabric, constantly scanning for weak spots and reshaping the network to minimize risk.

AI in cybersecurity becomes a multibillion-dollar engine

AI is not just a new feature in security products; it is becoming a dominant engine of market growth. Grand View Research estimates that the global AI in cybersecurity market was about 25.35 billion dollars in 2024 and is on track to reach nearly 93.75 billion dollars by 2030, driven by a compound annual growth rate of more than 24 percent. Grand View Research A separate generative AI cybersecurity report projects that AI-focused security offerings could grow to 35.5 billion dollars by 2031, fueled by AI supply chain attacks and demand for secure model execution. GlobeNewswire

These numbers reflect a shift in where enterprises expect value. AI is being applied to everything from malware detection and identity analytics to code security and policy verification. Behavioral analytics and user and entity behavior analytics (UEBA) platforms have already used machine learning for years, but generative and multi-agent AI expand the possibilities: automatically summarizing complex incidents, drafting incident reports, or generating recommended firewall and microsegmentation policies for multicloud deployments.

Education and governance are rising alongside this investment. Gartner has recently warned that “shadow AI”—unmanaged use of AI tools by employees—could lead to security or compliance breaches at roughly 40 percent of enterprises by 2030. IT Pro+1 That has pushed CISOs to treat AI as both a defensive asset and a source of new risk that must be monitored, guided, and constrained.

For multicloud networking, this evolving AI security stack is not a bolt-on. It will increasingly sit inside the PaaS layers that orchestrate connectivity, identity, and application deployment across providers, turning the network into a dynamic, context-aware enforcement plane.

Preemptive security in a multicloud world

Preemptive cybersecurity aims to shift security left in time and deeper into the architecture. Instead of passively watching for known signatures or anomalous behaviors, preemptive tools look for conditions that make attacks easier—misconfigurations, exposed credentials, vulnerable routes—and change them before an adversary can exploit them.

Gartner’s analysis of preemptive security emphasizes three verbs: deny, deceive, and disrupt. Gartner+2Cybersecurity Dive+2 Denial means closing off paths entirely, such as decommissioning unused public endpoints, tightening identity policies, or enforcing mutual TLS between microservices. Deception involves planting decoys—fake credentials, honeypot services, synthetic data trails—that confuse attackers and provide early warning. Disruption targets the attacker’s workflow, for example by invalidating tools, breaking command-and-control channels, or throttling suspicious automation.

Multicloud architectures complicate and amplify these tactics. A misconfigured storage bucket, overly permissive IAM role, or forgotten VPN tunnel in any one cloud can give attackers a foothold, even if other environments are well locked down. Preemptive security therefore depends on having a unified, AI-assisted view of configuration, identity, and traffic across clouds.

AI models can scan thousands of configuration baselines, access policies, and routing tables, learning what “normal” looks like in a given organization and highlighting deviations that correlate with past incidents. They can also simulate attack paths, applying frameworks like MITRE ATT&CK to predict likely lateral movement once a particular asset is compromised. Recent analyses of AI-driven autonomous detection systems show growing alignment between machine learning approaches and ATT&CK techniques, with research focusing on how reinforcement learning and agentic AI can adapt defenses in real time. Preprints+1

In practice, this means that by 2026, many organizations will have AI-driven engines continuously evaluating their multicloud topologies, identities, and workloads, proposing or implementing preemptive changes that reshape the attack surface daily.

Embedding AI-native defense into the PaaS stack

To be effective, AI-driven and preemptive cybersecurity cannot live solely inside isolated security appliances or external SaaS tools. It must be embedded into the same PaaS layers that manage application deployment, networking, and data flows across multicloud.

AI-native PaaS platforms, described in earlier articles in this series, already treat infrastructure as code and intent. They use generative AI to help engineers design topologies, generate policies, and troubleshoot issues. Extending that concept to security, the platform becomes the default place to express security intent: which data domains are sensitive, which workloads must never be exposed to the internet, which identities deserve just-in-time access, and which network segments require additional deception assets.

From there, AI models and multi-agent systems can translate that intent into concrete configurations across clouds. For example, if a team declares that payment processing microservices must be isolated under a strict zero-trust policy, the PaaS can automatically generate network policies, security groups, service mesh rules, and identity constraints across AWS, Azure, and Google Cloud that enforce that isolation. It can also continuously check drift, alerting when a new path appears that violates the declared intent.

Cloud Security Alliance research notes that many security teams see AI not only as a way to detect threats faster, but also as a way to reduce noise and automate repetitive tasks so human analysts can focus on higher-level decisions. Cloud Security Alliance+2Cloud Security Alliance+2 PaaS-integrated AI security agents can triage vast numbers of alerts, cross-correlate telemetry from different clouds, and surface a handful of incidents that genuinely need a human decision—along with a draft playbook of recommended actions.

The more these AI systems are wired directly into CI/CD pipelines, infrastructure-as-code repositories, and cloud control planes, the more naturally they can move from advice to action. That is where preemptive security becomes real: when the same platform that deploys applications can also safely roll out microsegmentation, rotate keys, and retire risky routes in minutes.

Agentic AI and multi-agent defense of the fabric

A new family of AI techniques—often described as agentic AI—is starting to change how security tools behave. Instead of simply classifying inputs or generating text, agentic systems can pursue goals, plan sequences of actions, call tools, and adapt based on feedback. TechRadar recently highlighted how agentic AI is emerging as a force in cybersecurity, helping with tasks such as alert triage, penetration testing, and vulnerability validation, while also warning about the need for strong governance. TechRadar

In the context of multicloud networking, multi-agent security systems will coordinate several specialized AI agents. One might focus on external attack surfaces, scanning DNS records, certificates, and exposed APIs across clouds. Another might continuously analyze east–west traffic flows inside VPCs and VNets, looking for unusual lateral movement. A third agent might specialize in configuration and identity, searching for toxic combinations of permissions that create hidden escalation paths.

These agents can share findings through a central orchestration layer, much like microservices call each other in a distributed application. When the external-surface agent finds a suspicious scan pattern, it can notify the traffic-analysis agent to watch for follow-up behavior and the identity agent to tighten access for at-risk accounts. Over time, reinforcement learning can refine their collaboration, favoring action sequences that historically reduced incident impact.

Crucially, agentic AI will not only defend; adversaries will adopt it too. MITRE and other security research groups have already begun tracking tactics where attackers use AI to generate phishing content, discover vulnerable assets, or automate lateral movement. Medium+2ThreatConnect+2 That puts additional pressure on defenders to match that level of automation and adaptivity, without giving up the human judgment that keeps automated systems from overreacting or causing harm.

Zero trust as the policy brain of AI-driven controls

AI-driven and preemptive security needs a policy framework to align its actions with business and regulatory expectations. Zero trust has emerged as that framework for many organizations. The NIST SP 800-207 publication defines zero trust architecture as a model where no network location is inherently trusted; every access request must be continuously authenticated, authorized, and encrypted, based on identity, device posture, and context. NIST Publications+2CyberArk+2

Vendors and practitioners have translated those principles into real-world patterns: identity-based segmentation, microsegmentation within clouds, per-request access decisions at API gateways, and tight integration between endpoint posture and access decisions. CrowdStrike+1 AI fits into this picture as an engine for context and enforcement.

For example, AI models can refine risk scores in real time. Instead of relying on static roles, they can evaluate behaviors—login times, resource access patterns, anomaly scores—and adjust access permissions accordingly. If a service suddenly starts making unusual east–west calls across clouds, an AI-driven zero-trust controller might temporarily restrict its network reach and require additional checks before restoring full access.

In multicloud networking, zero trust becomes the language that AI agents use to justify their decisions: restricting access not because “traffic looks weird,” but because a particular identity, device, or workload no longer meets a defined trust threshold. That clarity is essential for auditors, regulators, and internal stakeholders who need to understand why certain actions were taken.

Use cases: from east–west blind spots to AI supply chain risk

Several concrete use cases show how AI-driven and preemptive cybersecurity will reshape multicloud networking by 2026.

One focuses on east–west blind spots. As organizations adopt microservices and service meshes across clouds, internal traffic volumes grow rapidly. Traditional perimeter defenses struggle to see or interpret this east–west flow. AI models trained on normal communication patterns can flag unusual spikes, lateral scans, or data exfiltration attempts that cross cluster or cloud boundaries. When combined with preemptive policies, the system can automatically apply microsegmentation, route traffic through deeper inspection points, or isolate suspicious workloads.

Another use case addresses AI supply chain risk itself. As enterprises consume third-party models and AI APIs, they must ensure that prompts, outputs, and model weights are not exposing sensitive data. Generative AI cybersecurity research points to rising attacks on AI pipelines and model hosting environments, prompting investment in secure model execution and AI-aware application security. MarketsandMarkets+2GlobeNewswire+2 AI-driven security tools can inspect prompts and responses for sensitive content, monitor model behavior for drift or anomalous outputs, and enforce policies about which network segments are allowed to reach which AI services.

A third use case combines AI in cybersecurity with confidential computing, covered earlier in this series. Security teams can run sensitive detection algorithms inside hardware-protected enclaves, ensuring that even if an attacker compromises parts of the multicloud environment, they cannot tamper with or inspect the defensive models themselves. AI-driven preemptive tools then become part of the “crown jewels” protected by confidential computing and defended by zero trust.

Governance, bias, and the risk of over-automation

As with any powerful technology, AI-driven and preemptive cybersecurity brings non-technical risks that multicloud and PaaS leaders must address.

Governance is critical. The Cloud Security Alliance emphasizes the need for clear AI usage policies, continuous monitoring, and transparent reporting as organizations scale AI in security. Cloud Security Alliance+2Cloud Security Alliance+2 If AI agents can quarantine workloads, block routes, or revoke access at scale, enterprises must define who can configure these agents, how changes are approved, and how to roll back in case of errors.

Bias and blind spots also matter. AI models trained only on past incidents may perform poorly when novel attack techniques appear or when organizations shift to new architectures. Over-reliance on historical data can lead to missed threats in emerging domains or unjustified suspicion of legitimate behavior that happens to resemble past patterns.

Finally, over-automation is a real danger. Preemptive controls that act too aggressively can cause outages, block legitimate transactions, or create friction for developers and business users. The most successful adopters will keep humans in the loop for higher-impact actions, use progressive rollouts for new policies, and maintain strong observability so they can see exactly what AI-driven tools are doing across their multicloud networks.

Closing thoughts and looking forward

AI-driven and preemptive cybersecurity is poised to transform multicloud networking from a static set of rules into a dynamic, self-defending fabric. Instead of waiting for alerts, organizations will increasingly rely on AI-native PaaS platforms and multi-agent security systems that continuously scan for weaknesses, simulate attack paths, and reshape connectivity and access in near real time.

In this fifth article of the series, we explored how preemptive security, agentic AI, and zero trust will converge within PaaS to protect data and workloads that span multiple clouds and edges. Market forecasts point to rapid growth in AI security spending, while research communities like MITRE and the Cloud Security Alliance work to align AI-driven defenses with proven frameworks and best practices.

Looking ahead to 2026, the organizations that thrive will be those that integrate AI-driven security into the core of their cloud and network strategies rather than treating it as an add-on. They will invest in governance and observability, build cross-functional teams that understand both security and AI, and design multicloud topologies with preemptive defense in mind from day one. Done well, AI-driven cybersecurity will not only reduce risk; it will give enterprises the confidence to innovate faster in a world where attackers, too, are powered by AI.

Reference sites

Preemptive Cybersecurity Technologies Will Account for over 50% of IT Security Spending by 2030, Up from Less Than 5% in 2024 – Gartner – https://www.gartner.com/en/newsroom/press-releases/2025-09-18-gartner-says-that-in-the-age-of-genai-preemptive-capabilities-not-detection-and-response-are-the-future-of-cybersecurity

Embracing AI in Cybersecurity: 6 Key Insights from CSA’s 2024 State of AI and Security Survey Report – Cloud Security Alliance – https://cloudsecurityalliance.org/articles/embracing-ai-in-cybersecurity-6-key-insights-from-csa-s-2024-state-of-ai-and-security-survey-report

AI In Cybersecurity Market Size, Share & Trends Report – Grand View Research – https://www.grandviewresearch.com/industry-analysis/artificial-intelligence-cybersecurity-market-report

State of AI and Security Survey Report – Cloud Security Alliance / Google Cloud – https://services.google.com/fh/files/misc/csa_state_of_ai_and_security_survey_google_cloud.pdf

What Is Zero Trust Architecture (ZTA)? NIST 800-207 and Zero Trust Architecture – CyberArk – https://www.cyberark.com/what-is/nist-sp-800-207-cybersecurity-framework/

Benoit Tremblay, Author, IT Security Management, Montreal, Quebec.
Peter Jonathan Wilcheck, Co-Editor, Miami, Florida.

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