About €100 million was missing after a cyberattack at French lender Crédit Mutuel Arkéa, Reuters reported where attackers used one of the bank’s own accounts to make fraudulent transfers.
Arkéa paused some transactions and said nearly all operations had resumed after checks and tighter safeguards, according to its 4 November 2026 update.
Once a bank brings an AI agent back online to help with fraud reviews, its first case file could look much like the ones analysts saw before the attack. Would the analyst reviewing it spot a missed transaction check?
A backup might contain an older copy of the model, while the script for preparing its data was updated later.
The team can recover both pieces and still have to work out whether they ever ran together.
Commvault calls that missing history the context gap in its June 2026 report on AI agent recovery, The AI Resilience Blueprint.
How a False Detail Ends Up in a Fraud Case
Two agents might put the file together, one pulling the transaction history and account notes while the other writes the summary. An altered note can make it through both agents and turn up as evidence.
Agents that remember earlier tasks may carry the detail into later cases. With write access, they could save it in a system other teams use.
Just 17% of surveyed organisations continuously monitored exchanges between agents, while 38% monitored AI traffic end to end, figures from Akto’s 2025 State of Agentic AI Security survey.
Seeing the handoff helps a bank check whether each agent stayed within the rules it was given.
About one in five of the 3,235 business and IT leaders in a separate survey said their organisations had a mature approach to governing autonomous agents, according to Deloitte’s 2026 State of AI in the Enterprise research.
What Could a Bank’s Backups Leave Out?
Account notes may sit in a separate database while the model runs on a managed cloud service.
Having a backup of the model leaves the bank to check whether it can also recover the notes the agent relies on. Self-hosted model registries can sit outside that coverage, too, the report notes.
The report asks banks to trace the source and handling of training data because tampering can change how the model behaves once it goes live.
Restoring the model and data can leave an attacker’s changes to the agent’s permissions in place.
Before reconnecting the agent to customer systems, the team needs to check what it can read or change against the access the bank originally approved.
How Does a Bank Know Which Setup to Restore?
The recovery team can look up the version the bank deployed in the model registry, then match it to the data, instructions and permissions the agent used at the time.
Commvault’s report proposes using a system of record for AI agent recovery to tie those details together at a given time.
Paired with logs of what agents read and handed off, it could help the bank see which cases relied on an altered note.
Knowing when the note first turned up gives the team a point to work back from.
Isolated backups that preserve earlier versions and are protected against tampering can help the bank find a combination of software, data and settings that ran together.
The report calls the target a known-good state, a setup the team can verify before using it again.
What to Test Before an Agent Goes Live Again
One way to test AI agent recovery is to replay a fraud case the analysts have already reviewed.
The summary may read differently this time, but the analysts know which transfers should turn up and can check the logs to see what the agent read.
Missed transfers or searches of unrelated accounts give the team a reason to go back and check what it restored.
Recovery drills can also cover stolen credentials or tampered data, as the report recommends.
When the board, an auditor or a regulator asks why the bank put the agent back to work, the team can show which setup it restored and how the agent performed in testing.
Download The AI Resilience Blueprint for the full AI agent recovery checklist and a suggested order for working through it.
