Brand team enforces tone and messaging standards
Content policies block the AI employee from producing outputs that violate brand guidelines, ensuring every piece of content stays on-brand.
Built-in Content Safety
Five company-wide policies check every message in and out: injection attempts, unfit replies, personal data, leaked configuration, and off-limits subjects.
Your Security Officer evaluates everything your employees receive and produce. Five policies, each with its own switch. Input Safety stops messages trying to hijack an employee's instructions. Output Safety holds back replies that are toxic or unfit to send. PII Protection replaces personal data with markers. Data Leakage Prevention keeps your setup, tokens, and credentials in-house. Topic Control holds employees to the subjects you choose.
Topic Control is where you write your own rules, in plain language. Name the subjects an employee handles and anything unrelated is politely declined, or name the ones that are off-limits and they stay off-limits however the conversation gets there. A support team scopes an employee to product help, billing, and refunds. A regulated business rules out medical or legal advice. Both lists match on meaning, not exact words.
Policies evaluate both sides of the conversation: input before the employee starts, output before anything is sent. Every action is recorded with the policy that caused it, so you can see what has been caught, which policy is busiest, and read each individual message in a live inspector. Turn a policy on and it covers every employee across every team from their next message, including the ones you hire later.
Built-In Content Safety With Industrial-Grade Filtering
Sistava ships with content safety filtering powered by NVIDIA NeMo Guardrails, one of the most widely used open frameworks for guiding language-model behavior. This handles the baseline: blocking harmful, abusive, or off-topic outputs before they reach users or downstream systems.
Out of the box, filters cover toxicity, hate speech, prompt injection attempts, and off-topic deflection. These run at the model output layer, so even if an external input attempts to manipulate the agent, the response is filtered before it leaves the system. Your AI employees do not produce content you would be embarrassed to put your name on.
Custom Policies for Your Brand, Legal Team, and Industry
Every organization has content requirements that generic filters do not cover. A financial services firm cannot have agents giving investment advice. A healthcare company needs agents to avoid diagnostic claims. A brand may have a list of competitor names or product claims that must never appear in agent outputs. Custom Content Policies let you define these rules precisely.
Policies are defined as rules: if output contains X, do Y. Y can be block and replace with a safe fallback, flag for review, or log silently. Rules support keyword lists, regex patterns, topic classifiers, and semantic similarity checks for nuanced restrictions that keyword matching alone would miss.
Custom policies stack on top of the built-in NeMo filters, not instead of them. You always get the baseline safety layer plus whatever is specific to your organization. Changes to policies apply to all current and future AI employees in your workspace.
Content Safety Across the Entire AI Workforce
In a multi-agent environment, content safety cannot be agent-specific. When agents collaborate, delegate tasks, or pass outputs to one another, a gap in one agent's policy is a gap in the whole system. Content Policies apply at the platform level, covering every AI employee regardless of role or configuration.
This is especially important for customer-facing agents where brand consistency and legal compliance are non-negotiable. A sales agent, a support agent, and an onboarding agent all operate under the same content rules, so your customers get a consistent and safe experience regardless of which agent they interact with.
Use Cases
Platform team prevents misuse of AI capabilities
Policies define what topics, actions, and output types are off-limits, so the AI agent cannot be directed to produce harmful or inappropriate content.
Enterprise team meets regulatory content requirements
Industry-specific content rules are encoded as policies, and the AI employee enforces them automatically on every output it generates.
Education platform keeps AI outputs age-appropriate
Content safety policies filter AI agent outputs based on audience, ensuring all generated content meets the platform's standards for students.
Comparison
| Before | After |
|---|---|
| AI agents can generate anything, policy is manual and reactive. | Content policies are enforced automatically on every output. |
| Brand voice violations require human review of every piece. | Policies catch violations before content leaves the agent. |
| Compliance with content regulations is a manual process. | Regulatory content rules are encoded once and applied everywhere. |
| Misuse of AI capabilities is discovered after the fact. | Policies prevent out-of-bounds content at the point of generation. |
FAQ
What is NVIDIA NeMo Guardrails and why does it matter?
NeMo Guardrails is an open-source framework developed by NVIDIA for adding programmable safety and topical constraints to LLM applications. It is one of the most widely adopted tools in production AI systems for its reliability and flexibility. We use it as the foundational layer so you benefit from battle-tested safety infrastructure.
Can I prevent agents from discussing specific topics?
Yes. Topic-based restrictions are a first-class policy type. You can define off-limits topics using keywords, semantic classifiers, or both, and choose whether the agent deflects politely or simply declines to engage. This is useful for legal, competitive, or regulatory reasons.
Do content policies apply to agent-to-agent communication?
Yes. Policies apply to all outputs from AI employees, including messages passed between agents in multi-agent workflows. This closes the loophole where internal agent communication could bypass user-facing filters.
How quickly do policy changes take effect?
Policy changes apply immediately to new conversations. Ongoing conversations pick up the new policy at the next agent response. There is no deployment step or container restart required.
Can I block my AI agent from producing certain types of content?
Yes, Sistava includes built-in content safety powered by NeMo Guardrails, and you can add custom rules on top to block specific topics, outputs, or behaviors. Policies are enforced at runtime before any response is delivered.
We have strict rules about what our agents can and cannot say to clients. Custom content policies enforce all of them without us reviewing every response.