I work in security, at a company in banking & financial services.
AI agents are entering regulated, high-stakes work in every sector, and the failure modes rhyme. Pick your seat and your industry, and see the scenario, the question it raises, and how CompFly answers it.
A KYC agent cleared hundreds of accounts overnight, delegating document checks to a second agent nobody onboarded.
*Illustrative scenario
Security asks
Did the second agent inherit permissions it should never hold?
No. Every agent, including a delegate, carries its own cryptographic identity and starts from default-deny. Permissions never flow through a delegation, and every agent-to-agent call is decided before it executes.
AI agents in banking & financial services today.
The workloads agents are already taking on in this sector, and where governance has to reach.
KYC and onboarding
Fraud and case triage
Back-office operations
What banking & financial services leaders have to answer for.
The sector-specific challenges, each connected to the CompFly capability that resolves it.
Model Risk Management now covers agents, and examiners expect the same rigor they demand of models.
Every agent is risk-tiered, its conduct baselined, and every decision recorded as attributable evidence: a black-box agent becomes an auditable process.
An agent that handles sensitive transactions is one manipulated input away from an irreversible mistake.
High-consequence actions pause for human approval, and a payment outside the approved envelope, a new payee domain or a magnitude spike, is denied before execution.
Data sovereignty and confidentiality rules follow customer data into every model call.
Prompts, tool arguments, and outputs are inspected inline, and a hybrid deployment keeps the enforcement path and the data inside your environment.
For security teams: see a policy stop an action.
A live session on your stack: an agent attempts an out-of-policy call, and the control plane denies it before execution.