Agentive AI is changing the landscape of cybersecurity by introducing new threats from automated attacks, which has made strict access control and a Zero Trust strategy more important than ever. Many organizations give AI agents broad access to data to allow them to operate efficiently and quickly. However, this can create vulnerabilities that cybercriminals exploit. Agentive AI systems are being used in ways that make it easier for attackers to launch attacks, such as prompt injection, where attackers trick the AI into following harmful instructions. These attacks can involve making the AI read manipulated web pages that contain hidden orders, allowing attackers to extract sensitive data and send it to their own servers. To prevent this, it is best to limit the access of AI agents to only the tools and data they need to perform their specific tasks. The principle of least privilege, which means giving only the necessary access, is considered the best practice in this context. The rise of Ransomware 3.0 has been reported by 61% of Chief Information Security Officers (CISOs), who say that the use of AI has increased the risk of ransomware attacks. The lack of clear rules and control over data in AI adoption has led to the emergence of Ransomware 3.0, which uses AI to make attacks more powerful. In these attacks, ransomware goes beyond just locking systems. AI-powered tools help attackers automate phishing, speed up identity theft, and move across different systems at a scale that was previously unimaginable. The most worrying development is that attackers are subtly altering or falsifying data records, which can damage trust in the data itself. Security officers now face a fundamental change in how ransomware poses a threat, as AI can navigate complex systems and retrieve information on behalf of an attacker. To stay secure, organizations must adopt a cybersecurity resilience strategy based on the Zero Trust model, treating each AI agent as a high-risk identity. This includes using strong, non-phishable machine authentication, strict access controls, and continuous monitoring to protect the data that AI agents can access. Identity security is now the first line of defense against AI threats. In 2026, the exploitation of identity and access management weaknesses is expected to grow. Although multi-factor authentication (MFA) has improved security, attackers are adapting to these defenses. As a result, organizations need to move faster in adopting phishing-resistant MFA technologies like FIDO2 and Passkeys to ensure they are the only approved way to connect to systems. According to the AI Cyber Benchmark 2026 study, the level of preparedness of companies in terms of AI cybersecurity has improved, rising from 31% in 2025 to 45% this year. However, this still modest score hides a growing gap between the governance policies that are now being stated and the actual ability to respond to new threats. IT departments have focused on meeting compliance requirements, adapting risk management frameworks, and appointing dedicated officers. However, the actual implementation of these measures in information projects is limited, especially as agent AI develops rapidly. Unlike traditional chatbot models, agent AI can plan and carry out complex actions across an entire company's applications. The situation is changing: it is no longer just about protecting data from leaks, but securing decisions, authorizations, and API connections that are executed automatically. Only 33% of organizations include the specifics of autonomous agents in their risk analysis, and barely 15% have implemented an identity and access management (IAM) system that is truly adapted to these non-human entities. On the technical controls side, companies have developed good habits by conducting penetration tests on their AI systems before deployment. However, once the application is launched, the security level drops significantly. Maturity declines sharply when it comes to detecting an ongoing attack (40%) and responding to it (29%). Operational defense teams suffer from real blind spots: if nearly 9 out of 10 companies generate activity logs for their AI applications, only 8% of them send them to their security operations center (SOC). Due to the separation between data teams and cybersecurity teams, unusual behaviors go unnoticed by analysts. This lack of real-time visibility leaves organizations generally quite helpless in the face of model deviations or the most sophisticated attacks. Crisis management capabilities remain very immature: only 12.5% of companies have a formal incident response plan for their AI assets. Faced with this rapid technological development, cybersecurity must quickly move from static compliance to dynamic defense. To manage the coming months with an acceptable level of control, three strategic priorities must guide security managers. The first priority is to regulate agent AI by making non-human identity management a top concern. AI agents must be treated as full users, subject to strict control over their privileges, access to data, and the actions they are allowed to perform. The second priority is to reduce operational blindness by unifying oversight. AI application activity logs must be integrated into the SOC to detect unusual behavior. In parallel, companies must create formal crisis scenarios that include the risk of AI becoming uncontrollable and prepare mechanisms to quickly isolate suspicious systems. Finally, RSSI will need to master the changes in the AI supply chain. The increasing use of open models and internally hosted models offers more independence but shifts the responsibility of cloud supplier security to companies. The governance of the lifecycle of these models and the verification of their integrity will become the key to cybersecurity maturity.