Question 1

What does 'AI security' mean for enterprise LLM usage?

Accepted Answer

It depends which layer you mean. Model security tools probe a model itself for adversarial weaknesses - jailbreaks, extraction attacks, backdoors - before or during training. WalledAI addresses a different, and for most enterprises more urgent, layer: the data and governance security around every production LLM call. It masks sensitive data before a prompt leaves your boundary, blocks policy-violating queries in real time, and logs the full interaction for audit - regardless of which model receives it.

Question 2

Is WalledAI a red-teaming or adversarial-testing tool?

Accepted Answer

No. WalledAI is not a model red-teaming or adversarial-robustness testing platform. It is a runtime data-protection and governance layer that sits in front of any LLM (GPT, Claude, Gemini, Llama, or an internal model) and controls what data reaches it, what policies apply, and what gets logged. Many enterprises run a red-teaming programme against their models and WalledAI as the runtime control plane around them - the two are complementary, not competing.

Question 3

How does WalledAI secure data in AI prompts?

Accepted Answer

Walled Redact detects PII, PHI, financial identifiers, and proprietary business data across text, images, code, audio, and documents, replacing them with secure tokens before the prompt reaches any LLM. The real values are restored in the response for authorised users only. The model sees the structure of the request - the context - never the underlying sensitive content.

Question 4

Does AI security stop at the prompt, or does it cover agents too?

Accepted Answer

It covers both. A single agentic workflow can make 50+ LLM calls, each a potential exposure point. WalledAI intercepts every LLM API call - human or agent-initiated - applying the same masking, policy enforcement, and logging, so agentic pipelines don't create ungoverned blind spots.

Question 5

What audit evidence does WalledAI produce for AI security reviews?

Accepted Answer

Every interaction is logged with user identity, role, the policy version applied, entities masked, the enforcement decision, and the model's response disposition. The Governance Dashboard turns this into exportable evidence packages mapped to MAS TRM, the EU AI Act, PDPA, HIPAA, and SOC 2 - the artefacts a security review or regulator actually asks for.

Question 6

Can WalledAI's AI security layer be deployed on-premises?

Accepted Answer

Yes. The masking, policy enforcement, and audit-logging layer deploys on-premise, in your private cloud, or fully air-gapped, so sensitive data and security telemetry never leave your environment. No customer data is used to train models.

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