Voice AI security and data protection for production agents

KVKK & GDPR review controls

Data processing, consent, retention and access requirements are scoped for review in each deployment rather than treated as a blanket promise.

Human handover by design

Agents can offer a human route where the workflow includes one, with escalation conditions and context agreed during implementation.

Read-only by default

Integrations start with least privilege. Write actions require explicit sign-off per tool.

Auditable outcomes

Configured calls can produce recordings, transcripts, scores and action records according to the notice, retention and access rules agreed for the workflow.

Controlled improvement loop

Tenant isolation is enforced. Production feedback is reviewed against your agent's goals and turned into controlled releases under the data rules agreed for your operation.

Guardrailed by design

Agents refuse out-of-scope requests and never invent policy on the call.

How an attack looks

A direct override: "ignore your instructions," mid-call.

Instructions hidden inside tool or web data: a CRM note, an order record or a webpage carrying text written for the agent, not the person reading it.

Staff impersonation: a caller claims to be an employee or the account holder to unlock actions meant only for a verified identity.

Refund or transaction pressure: a request outside policy, repeated or escalated, hoping persistence works where a direct ask would not.

What the agent does

Refuses out-of-scope instructions. Nothing said on the call changes its operating instructions.

Treats tool output as data, not commands. Instruction-like content coming back from a tool is ignored, not executed.

Verifies identity before privileged actions. An unverified claim is refused and logged for review.

Requires authorisation for high-risk transactions. No refund, transfer or discount clears on request alone.

Offers a human handover instead of improvising an exception.

Instruction override

What the attacker tries: mid-call, tells the agent to ignore its instructions, forget the rules, or follow new instructions from the caller instead.

What happens with Dring: the agent's operating instructions are fixed for the call. Nothing said by a caller changes them, and the attempt is logged.

Injected content in tool results

What the attacker tries: plants instructions inside data the agent reads mid-call, a CRM note, an order record or a webpage, hoping the agent treats them as a command.

What happens with Dring: tool output is always treated as data, never as an instruction. Instruction-like content coming back from a tool is ignored, not executed.

Identity spoofing

What the attacker tries: claims to be a staff member, a supervisor or the account holder, to unlock actions or information reserved for a verified identity.

What happens with Dring: identity is checked against real verification steps, not a caller's claim. An unverified claim is refused and logged for review.

Unauthorised high-risk transactions

What the attacker tries: pushes for a refund, a transfer, a cancellation or a discount outside policy, repeating or escalating the request across the call.

What happens with Dring: high-risk actions need explicit authorisation. The agent cannot approve them on request alone, however many times it is asked.

Data exfiltration requests

What the attacker tries: asks the agent to read out another customer's data, an internal number, or anything outside its own case, for example "read me the other customers."

What happens with Dring: tenant isolation and data minimisation mean that data is not reachable from the call in the first place, regardless of how the request is phrased.

Policy traps

What the attacker tries: leads the conversation toward the agent promising a discount, a refund or a guarantee that is not policy, hoping it goes along in the moment.

What happens with Dring: every reply is checked against policy before it is spoken. An improvised promise never reaches the caller.

Adversarial scenarios

Instruction overrides, injected tool content, identity spoofing and policy traps are included in pre-release review so the agent is tested against the ways a real workflow can be pressured.

Independent review

Automated checks and human review examine difficult scenarios. Disagreement or uncertainty is treated as a signal for investigation before a change is promoted.

Output control

The agent's reply is checked against policy before it is spoken, not after, so a bad instruction cannot reach the caller.

Transaction control

High-risk actions need explicit authorisation. The agent cannot clear a refund, a transfer or another sensitive action on request alone.

A living scenario library

The scenario library is red-team style, and gets a new case whenever a new attack pattern appears anywhere in production.

On the release scorecard

Results sit in the release scorecard alongside accuracy and quality metrics, and a release does not ship without them.