Cyber security is at a tipping point. According to PwC Global Threat Intelligence, ransomware attacks have surged by 70% year-on-year. AI-driven deepfakes, phishing, and misinformation are now persistent, global threats. Even the best-trained experts struggle to distinguish deepfakes from reality. Attackers leverage AI for rapid exploit development, leaving organizations with little time to respond. Traditional awareness training and patch management can no longer keep pace. Attackers exploit vulnerabilities immediately upon disclosure with no time to patch. As AI adoption accelerates, so does the attack surface—exposing new vulnerabilities and technical debt in data security.
Key Priorities for Building an AI-Ready Cyber Architecture
Adapt to the Changing Cyber Security Market
While established security capabilities remain essential, organizations must evolve their toolkits. PwC’s Digital Trust Insights 2026 reveals that Network security modernization and Zero Trust are core investment areas, with significantly higher priority in Germany. At the same time, the seven largest security companies market share increased to more than that of the next 50 vendors. These vendors heavily extend their platforms with new features and automation across cyber security capabilities. Our digital agendas over the past years, including cloud strategies, remote work and now AI adoption dictate that security follows suit on derisking these new environments. Cyber security meanwhile is held back by trying to harmonize legacy IT estate control with modern and rapid business developments. Now that we do not store our precious data behind technical walls any longer and our business processes are operated from everywhere, technology modernization must happen in cyber security, too.
Implement the Cyber Security Revolution
The next evolution in cyber security leaves physical infrastructure behind. Artificial Defense leverages a software-defined perimeter, anchored to identity, consolidating access into unified management planes, regardless of device or network. Over the past years, we have tried to control data and connectivity on a hardware level and by putting more efforts on the same security concepts for granularity. A modern architecture enables granular, risk-adaptive controls without the overhead of managing countless enforcement points. Security restrictions now follow the risk profile of each access request, layering controls for true resilience. Technology harmonization finally delivers the control and efficiency organizations have long sought.
Automate Security with Artificial Defense Architecture
Automation is the cornerstone of Artificial Defense. Policy decision points orchestrate the flow from resource request to access, requiring a fundamental shift in mindset. Identities seamlessly span applications and data, while connectivity is managed point-to-point through the cloud. Coverage of assets becomes a key metric and cyber teams finally reach service-orientation. Every access follows its individual risk evaluation, based on real-time context from cyber domains. While zero trust is often portrayed as either a technology investment, or the evolution just to make conventional measures more granular, Artificial Defense focuses on deploying automation and AI for the benefit of defenders. Each access request is verified, including factors such as application risk, authentication security, location, behavior, and detections. Although it may sound complex initially, it actually reduces effort compared to the continuous improvement of traditional capabilities such as segmentation and asking business owners to implement long control catalogues.
Translate Governance into Operational Control
Artificial Defense translates governance requirements into actionable enforcement, realizing true control and effectiveness. For example, coming from a requirement on risk-based secure authentication, this is translated into an actual enforcement policy. The application risk score derived from the risk assessment process and augmented with technical data is integrated into the Identity Provider (IdP), where the policy is then set to request a strong second factor authentication whenever the risk reaches a defined level. Access can then vary dynamically, as application risk changes or access context poses risk from other factors, such as device compliance or location. Governance teams need to understand now that their requirements enforce direct restrictions in the business, while finally seeing compliance to the requirements framework. Security operations teams need to provide onboarding service to these policy decision points and mirror back policy impact on business back to the governance teams.
AI’s role in Artificial Defense
Automation remains central, but AI is rapidly expanding its role. Cyber security becomes increasingly resource-hungry as digital strategies drive dependency on distributed services and data. Cyber security must orchestrate risk reduction across SaaS, cloud, third-party, and outsourced environments. Only automation can manage this granularity. AI can help define these granular security enforcements and gain control over the manifold access scenarios in modern enterprises. AI helps to evaluate risk for applications, third parties and business processes, support teams with implementation guidance and augment cyber defense in detection and response.
What Does It Take to Turn AI-Driven Security Strategy into Action?
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