Identity, scoped credentials, sandboxed execution and per-action logging — the controls that bound what a compromised agent can do, since input filtering cannot
On ibl.ai you own all the code and the data, run it model-agnostic across any LLM, and pay with no per-seat pricing — so you can deploy anywhere, from your own cloud to a fully air-gapped network.
Last updated:
Agent security is a containment problem, not a filtering problem. Language models receive instructions and data through the same channel with no structural boundary between them, so no filter reliably distinguishes a developer instruction from text inside a retrieved document.
That means you cannot prevent an agent from being manipulated. You can only bound what a manipulated agent is able to do.
Everything in this guide follows from that: give each agent its own identity, grant narrow and short-lived authority, sandbox execution, require confirmation before consequential actions, and log every action against an identity so an attempt is detectable rather than invisible.
Agents become principals with short-lived, task-scoped grants. Most organizations extend what they already have rather than building an agent-specific credential store.
Every tool, system and dataset each agent can reach. Capabilities accrete faster than anyone re-scopes credentials, so this needs to be current rather than original.
Storage you control, with retention set by your obligations. An audit trail inside a vendor's platform is a reporting view, not evidence you hold.
Sending, publishing, deleting, paying and granting access. This list is what determines where human confirmation is mandatory.
Not a shared service account and never a borrowed human session. An agent inheriting a person's credential produces a log that positively misattributes machine actions to that person, which is worse than no log.
A static API key must cover the broadest thing the agent might ever do, which makes it over-privileged on every other day. Authority should be requested per task and expire.
These are the highest-value target in the environment.
Agents that execute code or call tools should do so in an isolated environment with an explicit allowlist, network egress controls and resource limits.
This is the control that stops a successful prompt injection from completing the damage. Reading is recoverable; sending, publishing, deleting and paying are not.
A confirmation nobody understands is a rubber stamp.
Input filtering and instruction hierarchies raise the cost of an attack but cannot close the class, because the space of natural-language phrasings that convey an instruction is unbounded.
Including indirect injection planted in retrievable sources.
Delegation cannot be reconstructed after the fact, so the record must be complete when it is written. This is what turns a successful attack from invisible into detectable.
Everything else raises attack cost. What an injection can actually achieve is bounded entirely by what the agent was already permitted to do, which makes scoping the load-bearing decision.
Audit regimes across regulated sectors assume a named actor behind every access. A shared service account fails that standard regardless of whether anything improper occurred.
Confirm too much and reviewers approve reflexively, which is worse than not confirming. Reserve it for genuinely irreversible actions and make the prompt evaluable.
If the permission model and guardrail logic are a vendor's, your security posture is bounded by their roadmap and described rather than inspected.
Credential inventory audited against the agent registry
Sampled reconstruction of historical actions from the audit trail
Audit trail queried for confirmed-action coverage
Red-team suite including indirect injection, re-run each release
Consequence: The agent gains everything the person can do, and the audit log misattributes machine actions to that person.
Prevention: Make each agent its own principal with delegation recorded explicitly, never a borrowed session.
Consequence: The first bypass produces an incident whose blast radius equals the agent's full permission set.
Prevention: Treat filtering as a cost-raising layer and verify that permission scope alone makes the worst case acceptable.
Consequence: An agent provisioned for the broadest possible task holds that access permanently, including during a compromise.
Prevention: Issue short-lived credentials scoped to the current task and dataset, expiring when the task ends.
Consequence: The record shows what happened but not who caused it or under what authority, which fails the attribution standard auditors apply.
Prevention: Capture agent, delegating human, scope and business context at write time — none of it can be reconstructed later.
ibl.ai is the agentic AI platform where you own all the code and the data. You self-host the entire stack inside your own perimeter, run it model-agnostic across any LLM and switch anytime, and pay by usage with no per-seat pricing — so you can deploy anywhere: your cloud, on-premise, GovCloud, or fully air-gapped.
Full source code under a perpetual license, running on your infrastructure. Not API access to someone else's platform — the stack itself is yours.
Run any LLM — Claude, GPT, Gemini, Llama, Command, or your own fine-tune — and switch providers without rewriting the platform.
Usage-based billing against a budget cap you set. Cost tracks what your organization actually uses, not how many people you employ.
Your cloud, your VPC, on-premise, GovCloud, or a fully air-gapped network with no outbound connectivity.
1.6M+ users across 400+ organizations run the platform this way, including NVIDIA, MIT, and Syracuse University.
ibl.ai is family-owned and operated from New York, NY — a U.S.-headquartered, domestically-owned long-term partner, not a vendor that sells licenses and moves on.
See how ibl.ai deploys AI agents you own and control—on your infrastructure, integrated with your systems.