Our 10 tips for improving the resilience of AI environments

Artificial intelligence (AI) is now at the heart of digital transformation. It is redefining business models, automating processes, improving customer relations and opening up new opportunities for innovation. Similarly, AI applied to cybersecurity is now one of the most dynamic and promising areas in our industry. These innovations undeniably provide powerful levers for efficiency and performance. However, like any technological advance, they have a downside: AI environments themselves are becoming prime targets. Due to their central role in modern organisations and the strategic value of the data they handle, they are attracting growing interest from cyber attackers, making their protection more critical than ever. Ensuring their continuity and operational integrity is therefore no longer an option, but a strategic requirement.

To help organisations improve the cyber resilience of their artificial intelligence environments, we offer the following ten key recommendations.

1. Adopt a holistic approach

Cyber resilience is not just a technical project: it is a comprehensive approach. It must be based on a detailed understanding of business priorities, critical dependencies and the regulatory framework in which the organisation operates. Building a holistic strategy involves defining the ‘why’, the “what” and the ‘how’: understanding the continuity challenges for the business, identifying the capabilities needed to address them, and integrating these elements into day-to-day operations. This framework must be supported by management, tested regularly and adjusted over time to keep pace with evolving threats and technologies.

2. Integrate resilience from the design stage of AI projects

Resilience must be considered from the moment models are created, not added after the fact. Every stage of the AI lifecycle—design, training, deployment, and operation—must incorporate security, robustness, and recovery requirements.

A ‘resilience by design’ approach prevents vulnerabilities from becoming ingrained in architectures or processes. It ensures that service continuity and resilience are taken into account from the very first lines of code. Furthermore, adopting a ‘resilience by design’ approach from the design phase is much more efficient than taking action after the fact.

3. Create governance dedicated to AI resilience

Setting up a dedicated task force is a key factor for success. This cross-functional team should bring together AI experts, data scientists, architects, developers and security managers. Together, they define resilience policies, action priorities and incident response plans. This integrated governance ensures consistency between business strategy and technical defence capabilities, while fostering a culture of resilience across the organisation.

4. Rely on an integrated and consistent framework

AI introduces new roles, processes and responsibilities. International standards such as those from NIST or ISO provide essential benchmarks, but they must be integrated into a broader framework. AI cyber resilience should not be managed as an independent silo: it must be aligned with existing security, crisis management and business continuity mechanisms. This integration ensures consistency, avoids duplication of effort and promotes a unified response in the event of an incident.

5. Identify and map critical assets

You cannot protect what you do not know. The first step is to establish a comprehensive inventory of AI-related assets: infrastructure, models, data pipelines, scripts, APIs, libraries and associated tools. This mapping allows you to assess dependencies, identify weaknesses and determine protection priorities. It forms the basis of any resilience strategy and facilitates the implementation of an effective response in the event of an incident.

6. Manage risks in a targeted manner

AI environments present specific risks: attacks on models, manipulation of training data, exfiltration of sensitive information, and parameter corruption. A realistic approach is to identify these risks, assess their impact, and define the level of risk that is acceptable to the organisation. Business impact analysis (BIA) helps to prioritise and scale protection or restoration measures according to the business's tolerance for disruption.

7. Reduce the attack surface

Identity compromises are among the most common attack vectors in AI environments. The proliferation of profiles and access points makes these systems particularly vulnerable. Reducing the attack surface requires rigorous identity and privilege management. Strict application of the principle of least privilege, role segmentation and enhanced control of privileged accounts help limit the spread of a compromise and ensure better access control.

8. Integrate business continuity into the AI strategy

Many AI projects move from prototype to production solution without being properly integrated into the business continuity plan. However, these environments must benefit from the same level of high availability and disaster recovery requirements as the company's critical systems. Protecting the data needed to run models, training checkpoints, caches and repositories ensures rapid recovery in the event of an incident and prevents the loss of major investments.

9. Prepare for reconstruction with a cyber recovery vault

Resilience also means the ability to get back up and running quickly after an attack. Setting up a cyber recovery vault, isolated from the production network and containing immutable copies of critical elements, is an effective approach to ensuring a controlled recovery.

This sanctuary, protected by an air gap, becomes the trusted base from which the company can restore its models and data after an incident. It embodies a pragmatic approach: accepting the possibility of an attack, but drastically reducing its consequences.

10. Strengthen the response chain and operational preparedness

Finally, operational resilience requires preparation. Response policies must be adapted to AI environments, with clear processes for identifying, isolating and rebuilding affected systems. Specific playbooks, proven recovery plans and regular table-top exercises allow teams to test their response and coordination capabilities. This practice develops the organisational reflexes needed to deal with a real crisis.

In conclusion

Cyber resilience applied to artificial intelligence is not just a set of technologies or procedures. It is a genuine strategic issue that links business, governance and technology. In a world where AI is increasingly driving business competitiveness, strengthening the resilience of these environments means protecting much more than just systems: it means preserving the very ability of the business to innovate, produce and inspire confidence.