If there is one thing I have learned after nearly four decades around this profession, it is that resilience never stands still.
The risks change. Technology changes. Organizations change. And every so often, something comes along that changes not only the threats we prepare for, but the way we practice resilience itself.
Artificial intelligence is one of those changes.
That is why I am excited to announce the release of the AI in Resilience Framework, version 1.0, developed by Disaster Recovery Journal with the DRJ AI in Resilience Advisory Council.
We did not set out to create another AI report or replace the standards our profession has spent decades developing. We wanted to answer a much more practical question:
What does AI actually mean for the resilience professional?
AI presents us with two realities at once.
It is a new source of risk. AI can accelerate attacks, create convincing deepfakes, introduce model and data failures, and allow autonomous systems to make bad decisions at machine speed.
At the same time, AI may become one of the most powerful tools our profession has seen. It can help analyze information, identify dependencies, maintain plans, create exercises, detect problems sooner, support recovery, and help crisis teams make sense of enormous amounts of information.
That dual reality became the foundation of the framework and its three pillars: Govern, defend, and apply.
Govern addresses accountability, standards, transparency, and trust. Defend looks at AI as a risk we must understand and prepare for. Apply focuses on responsibly using AI to strengthen resilience.
Within those pillars are ten domains and a five-level maturity model designed to give practitioners something we need right now: a common language and a place to start.
The timing is especially appropriate because several articles in this issue reinforce why this conversation matters.
Abbey Hernandez examines how AI challenges traditional continuity assumptions, including what happens when automation eliminates the human knowledge we have always depended on as a fallback. Nikita Saran explores AI's ability to strengthen crisis management while preserving human authority over consequential decisions. Michael Harding challenges us to move beyond documentation and prove our capabilities under real-world pressure. Stuart Murray makes a similar case for measuring and communicating actual business exposure rather than simply reporting completed activities.
There is a common thread through all of them.
Our profession cannot measure success simply by what we produce. We have to understand what we can actually do.
AI makes that distinction even more important.
Our job is not to become AI engineers. Our job is to understand how AI changes organizational resilience.
Where is AI becoming a critical dependency? What happens when an AI-enabled process is wrong rather than unavailable? Who can stop an autonomous agent? Can our teams operate if the AI tools they depend upon suddenly disappear?
And on the other side, where can AI make us better? Can it improve BIAs, keep plans current, identify hidden dependencies, create better scenarios, detect trouble earlier, and help us respond faster?
The AI in Resilience Framework gives our profession a structure for asking those questions.
This is version 1.0, and that is intentional. We want you to use it, challenge it, and help us improve it. DRJ has always been at its best when this community shares experiences and moves the profession forward together.
There could not be a better place to continue this conversation than DRJ Fall 2026.
See you in Dallas!
