AI agents & copilots
Scoped assistants grounded in your own data, with permissions enforced server-side rather than in a prompt.
Artificial Intelligence
Artificial Intelligence — Built to Ship, Reviewed to Be Safe
Cybrvault builds and secures custom AI systems: agents and copilots, document and workflow automation, and anomaly detection — each shipped with a security review covering prompt injection, data leakage between users, tool-permission scope and model access controls.
// when to call us
Signs you need artificial intelligence
Two things are true in 2026: AI can remove genuine hours of work every week, and most AI deployments we review leak data between users or let a crafted document instruct the model. We build the first without shipping the second.
- You want automation but cannot risk customer data leaving your control.
- An AI feature is already live and has never been security tested.
- Your team is pasting sensitive data into consumer chatbots.
- You need anomaly or fraud detection on data volumes humans cannot read.
// what you get
Deliverables, not slideware.
Workflow automation
Document intake, classification, summarization and routing wired into the tools you already use.
Anomaly detection
Models that surface fraud, misuse and unusual access across transaction and log data.
AI security review
Prompt injection, jailbreak, data-leak, tool-abuse and tenant-isolation testing with a remediation report.
// how it works
The engagement
- 01
Identify
Find the workflows where AI actually pays for itself, and the ones where it should not be used.
- 02
Prototype
A working proof of concept on real data within weeks.
- 03
Harden
Access control, isolation, logging, evaluation harness and red-teaming before production.
- 04
Operate
Monitoring for drift, cost and abuse, with iteration on real usage.
Who this is for
- Operations teams drowning in manual document work
- Product teams shipping AI features to customers
- Firms handling regulated data that need AI without exposure
- Security teams needing detection at machine scale
Typical investment
Real ranges, published up front. Final scope is quoted after a discovery call.
- AI opportunity assessment
- $2,500 – $6,000
- Custom build
- $10,000 – $75,000+
- AI security review
- $5,000 – $18,000
Workflow review, feasibility and prioritized roadmap.
Scales with integrations, data volume and compliance scope.
Prompt injection, isolation and tool-permission testing with retest.
// questions
Artificial Intelligence FAQs
What is an AI security review and why does my AI feature need one?
It is a penetration test aimed at the model layer: prompt injection through user content and documents, jailbreaks that bypass policy, data leakage between tenants or users, over-permissioned tools the model can call, and unsafe output handling that leads to XSS or SSRF. Traditional application testing does not cover any of these.
Will our data be used to train someone else's model?
Not in the architectures we deploy. We use enterprise API tiers with training disabled, or self-hosted models where policy demands it, and we document data flow and retention for every integration.
How long does an AI project take?
A working prototype on real data typically takes 3-6 weeks. Production hardening — access control, evaluation, logging and red-teaming — adds another 4-8 weeks depending on compliance requirements.
// go deeper
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Talk to an engineer, not a salesperson.
Fifteen minutes, no obligation, and you leave with at least one thing worth fixing — whether or not you hire us.
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