Generative AI has quickly become a force multiplier for cybercriminals and hackers. This powerful technology can automate the development of destructive malware, create realistic deepfakes, and build convincing social-engineering campaigns. As a result, the barriers to entry for less skilled actors are much lower, while sophisticated adversaries can operate at greater speed and scale. Unit 42 researchers have shown that AI-assisted attacks can go from compromise to data exfiltration in just 25 minutes, compared to two days for human attackers.
Unfortunately, the situation will worsen because of the emergence of agentic AI. While traditional ransomware and other attacks require preprogrammed scripts, AI agents can adapt their approaches and strategies in real-time. They can learn from defensive responses and evolve the attacks faster than humans can fight back. This is because AI agents have the ability to reason, plan and act autonomously.
But many organizations are not prepared for the agentic AI threat. They have based their cybersecurity strategies on the dangerous premise that all attacks can be prevented.
Then what needs to be done? Enterprises must make a fundamental shift in strategy. It’s about moving away from a prevention-only mindset to a model built on true resilience.
From Backup to Full Application Environment Rebuild
For decades, the bedrock for cyber recovery has been fairly straightforward: backup and restore. But modern ransomware makes this approach mostly useless.
Attackers not only encrypt systems but also corrupt recovery capabilities. This involves disabling or deleting snapshots, tampering with backup retention policies, and compromising orchestration layers. Even so-called “immutable storage” is not immune. Attackers can hijack the backup management plane and policies before the snapchats are activated. Or there can be exploits of configuration flaws.
In this brave new world, resilience demands the ability to rebuild an entire environment, such as rewinding data, applications, networks, identity frameworks, and their cloud dependencies. These artifacts are continuously scanned and then placed as trusted golden copies for clean point-in-time recoveries. By doing this, enterprises can recreate their IT ecosystems as they were before the compromise.
Without this capability, a compromise to a system can grind operations to a halt. Even a single day of downtime can result in millions in losses, regulatory fines, and reputational damage.
From Infrequent DR Drills to Regular Recovery Testing
For enterprises, disaster recovery drills are rare. Maybe there will be an annual tabletop exercise, mostly for check-the-box compliance purposes.
But IT resilience requires that recovery become part of an organization’s muscle memory. This means having frequent automated rebuild testing in isolated environments, say on a monthly or even weekly basis.
Cloud platforms make this practical and cost-effective. You can easily spin-up sandboxes for the rebuild testing and then tear them down when they are complete without having to reserve compute and storage capacity ahead of time.
Moreover, by using chaos testing in the rebuild drill in a cloned isolated environment, enterprises can validate how systems perform under real-world stresses. This can include simulating network failures, authentication outages, or corrupted dependencies.
Another useful strategy is to adopt the minimum viability framework. This is where the focus is on restoring mission-critical services, like identitysystems, order management, or patient care platforms. By tiering applications into mission-critical, business-critical, and non-critical categories, rebuild testing becomes practical and strategically aligned with business continuity.
From Siloed Playbooks to Cross-Functional Recovery as Code (RaC)
For many organizations, recovery planning is siloed. Security, IT operations, application teams, and business continuity groups each maintain their own runbooks. They are scattered across Word docs, ticketing systems, and PowerPoints.
The fact is that they are rarely tested holistically. This means that application dependencies, cloud configurations, and identity systems are often undocumented — that is, until it is too late.
An effective way to manage this is with Recovery–as–Code (RaC), which turns fragmented, static playbooks into auto–generated, version-controlled pipelines. RaC structures recovery processes as executable code that can be tested and evolved over time. This unifies security, cloud, and application teams around a single source of truth. It also provides for continuous improvement. Every rebuild drill updates the automation, making it more reliable for the next.
By adopting RaC, enterprises gain both speed and confidence. Failures that once caused days of downtime can be remediated in hours because teams aren’t scrambling to interpret documents under pressure. And because RaC is executed as code, it scales consistently across environments and aligns with modern DevOps practices. Ultimately, RaC transforms recovery from a theoretical exercise into a living capability—one that ensures the organization can withstand and recover from even the most advanced agentic AI-driven attack.
Avoiding Prevention Only
The rise of agentic AI means cyberattacks will only grow faster, smarter, and more relentless. Organizations that cling to prevention-only strategies will remain one breach away from disaster.
By embracing the three shifts—full environment rebuilds, regular automated recovery testing, and RaC—enterprises can replace fragile hope with proven active resilience. This isn’t just about surviving an incident; it’s about ensuring business continuity, protecting trust, and turning recovery into a true competitive advantage.
