🔒 Secure
AI Security — Protect What Your AI Agents Are Trusted to Do
AI agents don't just answer questions anymore — they call tools, move data, and take actions. That makes prompt injection an operational risk, not a curiosity. We build the detection, enforcement, and audit layer that sits between your agents and the actions they're allowed to take.
How It Works
Detect. Enforce. Prove.
Detect — LS-Stat
Enforce — Latch
Prove — Audit Chain
Deploy — Air-Gapped Ready
The Threat
Agentic AI needs a different kind of security
Your agent trusts its inputs
A retrieved document, a web page, an API response, or a customer message can carry instructions the model was never meant to follow — and a large language model cannot reliably tell content from command.
Traditional security tools don't look inside the context window
Firewalls and endpoint tools see network traffic, not what an AI agent is being told to do. Injection lives in exactly the layer conventional security doesn't inspect.
"Ask the model nicely" is not a control
Prompting a model to refuse malicious instructions is a mitigation, not a guarantee — it can be optimised against. A control that sits outside the model and enforces deterministically is a different category of defence.
Who Needs This
Wherever an AI agent can take an action, it needs a gate
Financial Services
Agentic systems that touch payments, account data, or core banking need a gate on tool calls, not just a well-behaved prompt.
Government & Public Service
AI agents handling citizen data and service delivery need an audit trail regulators and oversight bodies can actually inspect.
Healthcare
Diagnostic and administrative agents that call out to records systems need enforcement that fails closed, not silently.
Is your AI stack exposed to prompt injection?
Let's map where your agents call tools, where untrusted content enters, and what a gate and audit trail would look like for your systems. Free initial consultation.