Introducing Aegis LLM Shield: enforce security policy at every inference endpoint

Most organizations are attempting to secure their generative AI deployments by politely asking the model to behave.

Currently, production LLM deployments rely heavily on built-in safety filters and system prompts. From a security engineering perspective, this is an architectural anti-pattern. These internal controls are inherently fragile. They can be bypassed through targeted prompt engineering, they enforce the model vendor’s acceptable use policy rather than your organization’s compliance mandates, and they generate zero audit telemetry.

You cannot rely on a system to police itself when its core function is to generate novel responses based on unpredictable human input. 

The architecture requires an external layer that operates independently of the model’s internal weights.

We built Neysa Aegis to address security requirements across the full AI lifecycle, starting with our first release: Aegis LLM Shield.

Aegis LLM Shield sits outside the inference layer. It acts as a definitive security boundary on every interaction, inspecting prompts before they reach the model and evaluating responses before they reach the user. Built for the multi-model and multi-modal realities of enterprise environments, Shield gives security teams the exact visibility and operational control required to move AI from experimental to production grade.Already running AI in production?Request a demo

Why we built LLM Shield

Production LLM deployments typically rely on two controls: 

• A system prompt 
• The model’s built-in safety training

The assumption is that these two controls will keep things in line. They don’t. Here’s why:

• System prompts can be extracted. Attackers routinely probe models to surface their internal instructions. Once they know your system prompt, they know how to work around it.
• Safety training isn’t your policy. A model trained by OpenAI or Meta doesn’t know that your legal team’s AI assistant shouldn’t discuss pending litigation, or that your customer support bot must never quote pricing it doesn’t have authority to confirm.
• Neither logs anything. If something goes wrong (a sensitive document gets exposed, a user manipulates the model) you have no record of what happened or why.

The numbers reflect how real this has become. 

• Prompt injection is the #1 risk on the OWASP LLM Top 10
• 90% of successful injection attacks lead to sensitive data leakage
• Only 5% of organizations feel confident in their AI security readiness, even as 90% are actively deploying

Meanwhile, roughly 60% of enterprise AI initiatives stall before production due to data privacy and governance concerns.

Enter Neysa Aegis LLM Shield.

Aegis LLM Shield is the enforcement layer that sits outside the model, applies your policies, and gives your security team visibility.

What Neysa Aegis LLM Shield does

Every prompt is inspected before the model sees it. Every response is inspected before the user sees it.

Input controls (What it watches on the way in – user → model)

Output controls (What it watches on the way out – model → user)

Auditability and control:

RBAC: separate roles for global policy management and per-model administration

Every decision logged: filter fired, confidence level, verdict (block / redact / tag / allow)

PII masked in logs by default

Policy intensity tunable from the UI. False positives are expected with AI-based detection; the system handles reclassification without redeployment.

Add Guardrails to LLM Endpoints

Secure your LLMs with runtime protection against prompt injection, data leakage, and unsafe outputs.

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Who’s using it and how

• Internal copilots and knowledge assistants: prevent employees from accidentally leaking PII or proprietary data through AI assistants connected to internal knowledge bases. Enforce topic boundaries and response safety.
• Customer-facing chatbots and support tools: protect end-users from toxic or off-brand responses. Block prompt injection attempts from external users trying to manipulate your AI.
• LLM deployments in regulated industries: meet compliance mandates with configurable PII redaction, audit-ready logs, and policy enforcement aligned to GDPR, HIPAA, and India’s DPDP Act.
  
  

Aegis LLM Shield works with any model, on any cloud

LLM Shield is integrated with Neysa Velocis, but it connects to whatever you’re already running.

Universal API: models on GCP, AWS, Azure, on-prem, or any third-party API connect in five lines of code. No infrastructure changes. Your models stay where they are.

Neysa Velocis: LLM Shield is a toggle in theVelocis Developer Hub. It inherits your existing RBAC, workspace structure, and team roles automatically. Policy config, violation logs, and endpoint health are in one view. No separate tool, no separate invoice.

Same detection engines and policy controls either way.

What’s next

LLM Shield is the first release under Aegis. The next track covers red teaming capabilities and agentic security controls, for teams running models that have tool access, persistent memory, or operate across multi-model pipelines. We’re already working with early customers on this scope.

LLM Shield is not a silver bullet – the variety of human prompting is nearly infinite; but it provides the visibility and operational control necessary to use these tools responsibly.

Get started

Request a demo. If you’re on Velocis, LLM Shield is available as an add-on from the Developer Hub today.

Want to see it first? The self-guided walkthrough takes under ten minutes: provisioning, configuring input and output guardrails, enabling Shield on a live endpoint.