The Reality Behind the Hype

Artificial intelligence is everywhere in today's business conversations, and the promises are big: true transformation on a scale we have never seen as humans. The current reality is not that.

Today, when evaluating the real use and benefits of AI, it's mostly about automation and augmentation, resulting in two significant challenges for most companies:

  1. Trying to control their users in how they use AI at will (with lots of corporate data ending up in the public domain)
  2. Trying to gain enough operational control so they can implement and adopt AI where it really transforms the company's trajectory
The lack of controls is bringing a lot of initiatives to a screeching halt. Yes, there's a lot of positive movement because of AI, but we're also adding — at an alarming rate — to the illusion that we're more productive, more innovative. Are we, and at what cost?

More Capability, Less Control

The story that AI would neatly replace workflows has given way to a more complicated reality: Automation and augmentation are landing, but governance hasn't caught up. Users experiment across tools with most of them not authorized by the company (shadow IT). Sensitive content travels to places it shouldn't.

Shadow usage adds pressure. Employees paste sensitive content into external tools, export conversation logs, or replicate data to "just test something." None of this is malicious, but it erodes governance. If you don't know where your data went, you can't control how it's used — or recover it cleanly when something breaks.

So, are you more secure today than yesterday? Right now, the honest answer is this: not yet. Risk and costs are rising because control is uneven — or non-existent — especially when AI systems (and the humans using them) can change, move, or delete data at scale.

What Matters Most: Behavior, Not Model Debates

You can build AI on deterministic/statistical components or on generative models. That matters for predictability, but what matters most for SaaS data protection is behavior:

- Non-agentic uses analyze, classify, or advise. They don't take actions; the blast radius is small.
- Agentic uses take actions through tools or APIs — archiving, moving, or deleting content. The blast radius is larger because the system is allowed to change things.

Put plainly: The more freedom an AI system has to change data, the greater the potential damage if it goes wrong — so approvals, logging, and rollback must be in place. To automate actions and insights broadly, you need to be able to make changes reversible.

Design for Reversibility

The core risk isn't that models evolve. It's that organizations give up control when decisions and data flows are mediated by third-party AI tools. You gain capability, but you lose sovereignty and clarity on where data went, who accessed it, and how to unwind unintended changes.

You won't be able to eliminate surprises in this journey, but a key safety measure is considering whether you make them reversible. With SaaS backup solutions, immutability is an absolute non-negotiable core capability to deliver a reliable data protection service.

Here's how to stay in control:

- Keep independent, immutable copies of critical SaaS data across tenants and workloads
- Maintain point-in-time truth you can restore to — quickly and precisely
- Version and log the things that shape automated behavior (policies, prompts, and jobs)

If you can't roll it back, you don't control it.

Recovery as a Responsibility

As AI adoption grows, many incidents won't be malicious — they'll be unintentional consequences: a misclassification that moves content, an automated job that wipes a workspace, a policy that over-archives. Staying in control means being able to put things back accurately:

- What comes back (which sites, channels, mailboxes, records)?
- From when (the "last known good" that aligns to the event)?
- In what order (for example, identity and access configurations before collaboration data)?
- With what approvals (bounded automation with a human in the loop)?

Relying on recycle bins is not a backup strategy; for AI resilience, you need independent, immutable copies that allow precise rollback to a last known good state.

Architecture First: Independence and Predictable Economics

If you want reliable outcomes and rapid recovery, you need access to the full breadth of organizational data — backups, archives, historical states, and change metadata become strategic assets. Two principles matter here:

  1. Independence. Keep durable copies of critical data in an independent, immutable store. In cloud data, that means placing backup data in infrastructure that is separate from the production SaaS provider.
  1. Predictable economics. Choose a third-party backup that keeps data always hot and directly accessible 24/7 — with effectively unlimited retention — so you avoid rehydration penalties, tier-hopping, and surprise retrieval costs.
Data is the asset; control is the moat. Independent, immutable architecture is how you keep that control.

A Note on Security in the Age of AI

Has AI made the world more secure? The answer is no. Security budgets have increased year-over-year for the last 10 years, yet in 2025, we're likely to be the least secure we've ever been — both as individuals and as companies. What AI has done is significantly increase complexity and scale of the problems.

Attackers get new tools; defenders inherit new surfaces. But disciplined architecture — independent, immutable data storage — lets security posture improve alongside capability. The goal isn't absolute prevention; it's cyber resilience.

TL;DR

- AI automation is real, but governance hasn't caught up—shadow IT and data drift are major risks
- Agentic AI can change/delete data at scale; immutability and rollback are critical controls
- Independent backup with point-in-time recovery is the foundation of AI resilience
- Data is the asset, control is the moat—without reversibility, you don't own either

Source: Keepit Blog: AI of today: Power, drift, and the discipline to stay in control