By Shiva Pillay, General Manager & Senior Vice President, Worldwide, Veeam Software Data and AI solutions and Sandeep Bhambure, Vice President & Managing Director, India & SAARC, Veeam Software
An AI agent can make hundreds of legitimate changes before anyone discovers the one action that caused an incident. Restoring yesterday’s systems may remove the bad change, but it may also erase everything the business did correctly afterwards. That is why conventional recovery time targets are becoming an incomplete measure of resilience.
When AI shifts from advising employees to acting on their behalf, resilience must increasingly be measured by precision recovery: what an organisation can identify, reverse and recover, not simply what it can bring back online.
Precision recovery, tailored to individual agents and workflows, can give teams greater confidence to expand their use of AI, providing a safety net when something goes wrong and reducing the risk that a single incident derails an entire AI initiative.
Precision recovery is not a replacement for traditional disaster recovery. Organisations will continue to need broad restoration capabilities for major outages and cyber incidents. The challenge in AI environments is that a system may be functioning correctly while a single automated action has created business risk. In those situations, the ability to identify and reverse a specific action becomes as important as restoring an entire workload.
New agentic world, new objectives
As AI adoption accelerates, it is easy to focus on productivity gains while overlooking how fundamentally it is changing recovery requirements. Historically, recovery meant restoring systems and applications quickly. In an agentic environment, organisations must also be able to restore the AI agents and workflows operating across those systems to a trusted state.
McKinsey research found that over 60% of organisations are experimenting with AI agents. Many can post, deploy or modify information across connected environments, meaning a single incorrect action can quickly affect multiple systems.
This is where recovery needs to move beyond point-in-time restoration towards surgical precision. If an AI agent deletes, modifies or corrupts data, organisations need to be able to identify the specific action and reverse it without rolling back everything else.
There is also a fundamental question of authorisation: was the AI agent actually permitted to access or act on that data? As agents gain access to more sensitive enterprise information, organisations need visibility into what an agent accessed, what it was authorised to do and how an incident can be remediated.
For Indian enterprises, particularly as AI moves into production, this ability to demonstrate control matters as much as recovery itself. Under India’s DPDP framework, organisations will need clearer visibility into where personal data is held, how it is accessed and what controls are in place when something goes wrong.
Giving teams the safety net they need
Validated recovery objectives, supported by regular testing and evidence that recovery works in practice, provide a stronger measure of resilience in agentic environments.
This is not simply about deploying AI but having confidence in the underlying data and infrastructure to scale it safely. Recent industry research reflects this gap: while most organisations are exploring or deploying AI, far fewer believe they have the technical foundation to scale it safely. That confidence depends on knowing that the data feeding AI is trusted, access is controlled and there is a reliable safety net when an agent takes an unintended action. Precision recovery allows organisations to reverse specific incidents with minimal disruption, rather than relying on broad restoration.
The same principle can be applied to environments such as Microsoft 365, where Veeam’s connectors enable organisations to strengthen resilience and, where required, surgically roll back specific changes rather than restoring the entire environment.
Trust is the new name of the game
This means moving from system-level recovery towards individual changes and actions. By using attribution, tracked objects and record-level change data, organisations can identify what an AI agent interacted with and reverse the specific action that caused the incident.
This shift is also relevant as Indian enterprises modernise their infrastructure. Mission-critical workloads are moving from traditional hypervisor environments to alternative platforms and containerised environments such as Kubernetes, particularly in sectors such as financial services. As applications move between platforms, organisations need recovery capabilities that can protect workloads wherever they run and maintain resilience throughout the transition
Ultimately, the next phase of AI adoption will be defined by operational confidence proof that data is trusted, AI actions are controlled and mistakes can be precisely reversed. For Indian enterprises, that shift from assumed resilience to proven recovery is what will turn AI from a promising technology into a trusted, production-scale capability.