Controls
Security & Trust in Agentic AI
An autonomous system with your permissions is an employee you never interviewed.
The right mental model for agent security is not 'a feature with a bug.' It is 'a member of staff with system access.' You would give that person least-privilege access, a manager, an audit trail, and a clear list of things they must never do. Do the same here.
We will not put an agent into production without this in place. It is not an upsell; it is a precondition.
Least privilege, enforced by the platform
The agent runs as a user. Its permission set defines its reach. If it should not be able to delete an opportunity, do not grant it delete on opportunities and then ask it nicely in the prompt.
Prompt injection is a real threat
A customer can write instructions into a case description. If your agent reads that field and treats it as guidance, you have a problem. Retrieved content must be data, never instruction — and that has to be tested adversarially.
The Trust Layer
Dynamic grounding, PII masking, zero data retention with the model provider, toxicity screening, and an audit trail. Configured, evidenced, and reviewed with your risk team.
Observability is a security control
If you cannot reconstruct what the agent did and why, you cannot investigate an incident. Trace every run, retain it per your policy, and export it somewhere your security team already looks.
Also in why agentforce
What Is Agentic AI
Not a chatbot, not a copilot. The difference is who decides what happens next.
Read How it worksAgentic AI Architecture Explained
Five stages, one loop, and the places where each of them breaks.
Read The numbersROI of AI Agents
Model it with your own volumes. Ours would only flatter us.
ReadStart with the assessment.
Three to four weeks, a fixed fee, and an answer we are willing to say out loud — including if the answer is no.