Walled Protect
Content SafetyGuardrails.
Real-time content filtering across all LLM interactions - including agentic AI workflows. Block prompt injections, toxic content, off-topic queries, and policy violations before they reach your models.
Backed & Trusted By Industry Leaders
Threats We Neutralize
Every AI interaction is a potential attack surface. Walled Protect applies multi-layered defense against the full spectrum of LLM threats.
Block Rate
Prompt Injection
Detect and block adversarial prompts designed to manipulate LLM behavior, extract training data, or bypass safety mechanisms. Including indirect injection via tool outputs.
Languages
Toxic Content
Filter harmful, offensive, or inappropriate content in both inputs and outputs. Covers hate speech, violence, sexual content, and self-harm across 20+ languages.
Policy Enforcement
Off-Topic Queries
Ensure LLMs stay within defined business boundaries. A customer support bot shouldn't answer questions about competitors or generate investment advice.
Violations Blocked
Policy Violations
Enforce organizational policies in real-time. Custom rules for industry-specific compliance - no financial advice from a marketing bot, no medical claims from a wellness app.
Dual-Layer Protection
Input Guardrails
Every query - from employees, customers, or AI agents - is scanned before it reaches the LLM.
- Prompt injection detection & blocking
- Off-topic and scope violation filtering
- Toxic/harmful content screening
- Data leakage prevention (works with Walled Redact)
- Custom business rule enforcement
Output Guardrails
Every response from the LLM is validated before it reaches the end user or downstream system.
- Toxic/harmful response blocking
- Hallucination flagging (works with Walled Correct)
- Brand safety & tone compliance
- Regulatory language enforcement
- PII leakage detection in responses
Guardrail in Action
Dual-layer protection: every input is scanned and every output is validated before delivery
Block harmful content. Govern AI agents.
From prompt injection attacks to off-topic misuse - Walled Protect is your real-time content moderation layer.
Guardrails for Autonomous Agents
When you deploy AI agents, the risk surface expands exponentially. Unlike human users, agents make dozens of LLM calls per task, access external tools and APIs, and process data autonomously. You lose visibility into what data reaches external LLMs.
Consider a supply chain agent that queries inventory databases, checks supplier pricing, and drafts purchase orders. Without guardrails, your supplier negotiations, pricing strategies, and inventory positions are exposed with every API call.
Walled Protect intercepts every agent-to-LLM interaction, applying the same content safety policies that protect human-initiated queries. No blind spots. No ungovernable workflows.
Agent Governance Features
- Intercept all agent-to-LLM API calls
- Apply content policies per agent type
- Real-time monitoring of agent behavior
- Automatic escalation on policy violations
- Tool-use permission controls
- Full audit trail of agent interactions
- Multi-hop reasoning chain validation
- Custom guardrails per agentic workflow
Real-World Scenarios
Content safety isn't theoretical - these are real risks organizations face when deploying AI at scale.
Banking: Customer-Facing Chatbot
A bank deploys an AI assistant for retail customers. Without Walled Protect, adversarial users discover they can jailbreak the bot into providing unauthorized financial advice, generating fake account statements, or revealing internal process documentation. Walled Protect blocks these attempts in real-time while letting legitimate queries through seamlessly.
Healthcare: Clinical Decision Support
A hospital deploys AI for clinical note summarisation. The AI must never make treatment recommendations, prescribe medications, or provide diagnoses. Walled Protect enforces these boundaries at the content level, ensuring the AI stays within its approved scope regardless of how physicians phrase their queries.
Enterprise: Internal Knowledge Base
A company deploys an AI assistant over its internal wiki. Employees from different departments have different access levels. Walled Protect ensures the AI doesn't surface HR data to engineering teams or share executive strategy documents with junior employees - all enforced at the interaction level, not just at the document level.
Proven at Scale
1M+ Conversations Protected for a Single Customer
A leading APAC enterprise deployed Walled Protect across their entire AI stack - covering employee-facing tools, customer-facing chatbots, and internal agentic workflows. Within the first quarter, over 1 million conversations were processed with privacy guardrails, off-topic filtering, and hallucination detection active on every single interaction.
Conversations Governed
Policy Violations Blocked
Average Latency Added
False Positive Escalations
Related Resources
RED-EVAL: Adversarial Red-Teaming for LLMs
Our published research on adversarial testing methodologies that power Walled Protect's prompt injection detection.
Read more about RED-EVAL: Adversarial Red-Teaming for LLMsRead moreContent Safety in Agentic AI Systems
Why autonomous AI agents require fundamentally different guardrails than human-initiated interactions.
Read more about Content Safety in Agentic AI SystemsRead moreThe Cost of Unguarded AI
Case studies showing the financial, reputational, and regulatory impact of AI safety failures.
Read more about The Cost of Unguarded AIRead moreSecure every AI interaction in real-time
See Walled Protect in action - from prompt injection blocking to agentic AI governance.
Frequently Asked Questions
Authoritative references
Primary sources behind the standards, regulations and research referenced on this page.
- OWASP Top 10 for LLM ApplicationsRecognised list of generative AI risks including prompt injection.
- MITRE ATLAS adversarial AI knowledge baseTactics and techniques used in real-world attacks on AI systems.
- NIST AI 600-1 Generative AI ProfileGenerative-AI-specific risks and suggested actions from NIST.
- NIST AI RMF PlaybookImplementation guidance for AI RMF functions and controls.