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AI security
AI security is not only model safety. It is tool, data, agent, and workflow defense.
AI systems can expose secrets, follow poisoned sources, trigger unsafe tools, preserve stale automation, or drift into actions nobody approved. Folium reviews the whole AI operating path.
Buyer search intent
What this page is built to answer.
A buyer is worried about AI security, unsafe automation, prompt injection, data exposure, agent permissions, or hidden technical risk.
Question
Where can AI expose private data?
Question
Can agents be tricked into unsafe actions?
Question
How do we find stale or hidden automation?
Question
What should be reviewed before AI touches business systems?
Folium answer
The answer is a controlled operating path.
Folium turns the search problem into a decision-ready workflow: what to inspect, what to build, what to govern, what to measure, and what the business should own after launch.
01
Review prompts, retrieval sources, tools, permissions, dependencies, telemetry, logs, and secrets exposure.
02
Harden agent permissions, data boundaries, and state-changing action gates.
03
Identify stale automation and dark code that no longer has an owner.
04
Create containment, rollback, recovery, and relaunch plans.
Delivery workflow
How Folium moves from search intent to working capability.
The work is deliberately sequenced so the buyer can see the pressure, approve the boundary, inspect the build, and decide the next stage.
01
Surface review
Inspect AI entry points, tools, routes, prompts, sources, uploads, outputs, and external service paths.
02
Permission hardening
Separate read, write, approval, admin, external API, provider, and data-class boundaries.
03
Adversarial checks
Test prompt injection, retrieval-source poisoning, unsafe action attempts, secrets handling, and dependency risk.
04
Recovery plan
Create incident triage, containment, rollback, failed-case repair, and relaunch readiness.
Useful outputs
What a serious buyer should expect to receive.
These are the artifacts that turn AI interest into something a business can inspect, challenge, fund, support, and improve.
AI security exposure review
Dark code and stale automation map
Agent permission hardening plan
Prompt and retrieval-source defense checks
Incident recovery path
Related Folium paths
Go deeper from this buyer need.
FAQ
Questions this search usually hides.
These answers keep the page useful for humans while giving search engines and AI answer systems a clear view of the service boundary.
What is dark code in an AI environment?
Dark code is automation, integration, script, prompt, route, or tool behavior that still affects the business but lacks clear ownership, documentation, review, or active support.
Does AI security include prompt injection?
Yes, but it also includes retrieval-source poisoning, agent permissions, secret exposure, dependency risk, telemetry review, state-changing actions, and recovery.
Can Folium review existing AI systems?
Yes. Folium can audit existing AI tools, agents, controlled-retrieval stores, automations, APIs, and workflows to identify unsafe or unsupported areas.
Start here
Turn the search into the first reviewable workflow.
Folium can help translate this need into scope, architecture, data boundaries, working surface, evaluation, governance, and a practical next-stage decision.
Common questions
Questions this page answers.
What is dark code in an AI environment?
Dark code is automation, integration, script, prompt, route, or tool behavior that still affects the business but lacks clear ownership, documentation, review, or active support.
Does AI security include prompt injection?
Yes, but it also includes retrieval-source poisoning, agent permissions, secret exposure, dependency risk, telemetry review, state-changing actions, and recovery.
Can Folium review existing AI systems?
Yes. Folium can audit existing AI tools, agents, controlled-retrieval stores, automations, APIs, and workflows to identify unsafe or unsupported areas.