Stop the attempt before it runs
How do you keep AI agents from reaching data or taking actions they shouldn't?
Kyver puts a policy layer between your AI tools and your systems. Every request an agent makes, whether it reads a record, calls an API or runs a command, is checked against what that agent is allowed to do. Requests outside its permissions are blocked before they reach your data, and every refusal is logged.
- agent
- support-agent
- read
- crm.tickets, kb.articles
- write
- none
- egress
- none
- 10:02:11READREAD crm.tickets #18842allowed, in scopeallowedin scope
- 10:02:12READREAD kb.articles "refund policy"allowed, in scopeallowedin scope
- 10:02:13SCANSCAN attachment order_issue.htmlflagged, injected textflaggedinjected text
- 10:02:14READREAD billing.cards, customer 18842refused, rule scope.readrefusedrule scope.read
- 10:02:14WRITEWRITE crm.tickets #18842 owner=externalrefused, rule scope.writerefusedrule scope.write
- 10:02:15CALLCALL http.post to unlisted domainrefused, rule egress.nonerefusedrule egress.none
3 requests refused before execution
None reached billing, the ticket record or the network. Each refusal is logged with the rule that stopped it and sent to the security review queue.
How Kyver handles AI security
Least-privilege access for each agent
Each agent gets its own identity and a written scope: which systems, which records, read or write. No agent inherits a person's full permissions.
Intercept at the call
Agent requests pass through a gateway we deploy. Anything outside scope is refused before it executes, so the outcome never depends on the model choosing to behave.
Screen what the model reads
Content arriving from email, documents and the web is screened for instructions aimed at the model. Tool calls triggered by that content get extra checks.
Close the ordinary gaps too
The assessment still looks for exposed keys, open storage and stale accounts. They are the first thing an AI-assisted attacker will find.
Locks are getting easier to pick
AI makes attackers faster. Models can already find and chain together weaknesses that used to take a skilled person days, and every release gets better at it. A loose permission or an exposed credential is easier to exploit each quarter.
Finding and patching every weakness is worth doing, and it is never finished. The other half of the problem is the AI you deploy yourself. An assistant with broad access will eventually be asked, by a user or by text hidden in a document it reads, to fetch something it shouldn't. We work on that half: we catch the attempt and stop it before it executes.
Built in alongside security
- GovernanceDecide which AI actions need a human sign-off, and make sure nothing irreversible happens without one.
- CostRun most AI work on hosted models that aren't metered by the token, so the monthly cost is predictable.
- TraceabilityRecord every AI session step by step, so failures can be investigated and fixed instead of guessed at.
- MaintainabilityAn outside team with no internal politics, that stands behind what it builds and keeps improving it.
Questions about AI security
What is an AI policy gateway?
A service that sits between AI models and the tools they can use. It checks every tool call against a policy (who is asking, what they want to touch, whether it's allowed) and blocks or escalates anything outside the rules.
Do we have to replace our existing security tools?
No. The gateway works alongside your identity provider, firewall and endpoint tools. It covers something they weren't built for: actions requested by AI agents.
What is prompt injection?
Text written to manipulate an AI model, often hidden inside an email, web page or document the model reads. It tries to make the model leak data or misuse its tools. Limiting what each agent is allowed to do keeps a successful injection from causing damage.
Start with the assessment
Fixed price, agreed up front. Implement the plan with us within 6 months and we refund the fee.