Cheap regex checks that catch obvious AI content violations first
Deterministic regex, blocklist, and allowlist checks against your forbidden phrases, approved claims, and regulated-vertical rules — catches obvious violations before the more expensive AI compliance check runs.
The problem
Your AI compliance scoring is expensive — every output call costs cents and you produce thousands of outputs a week. You want to catch the obvious violations cheaply, with deterministic rules, before paying for the AI check. Your forbidden phrase list has 2,000 brand-banned phrases. Your approved claims library has 300 substantiated claims. Your regulated-vertical blocklist has 400 prohibited terms (FDA cure-claim language, FTC guarantee language, FINRA performance-claim language). Today you use a Node.js bad-words package and a Python script your intern wrote.
The categories of tools that touch this are mostly for the wrong problem. Network content filtering (Cisco Umbrella, Fortinet FortiGuard, Barracuda Web Filter, DNSFilter, WebTitan) filters internet traffic at the IT-managed network gate — different category. LLM guardrails (Guardrails AI, NVIDIA NeMo Guardrails, Lakera, Robust Intelligence) pre-filter LLM input and output against general AI safety prohibitions like hate speech and prompt injection, not your specific regulatory rules. Content moderation services (WebPurify, Bad Words API, ProfanityCensor, Hive, Telus, TCS, Genpact) are built for user-generated content. Open-source regex libraries (bad-words npm, profanity-check Python, Filter.js, PCRE, RE2) need your team to build and maintain the rule library. Your in-house compliance reviewer authors regex manually and iterates slowly.
The gap is a deterministic pre-filter that loads your specific forbidden phrases, approved claims, and regulated-vertical rules and runs them against every AI prompt and output across your stack — cheaply, before the AI check runs.
What success looks like
Your forbidden phrase list, approved claims library, and regulated-vertical rules load into the pre-filter. Regex, blocklists, and allowlists run at two points.
Before generation: every AI prompt gets pre-filtered before the LLM call. Prompts that contain forbidden phrases route to review instead of generating expensive output. This saves the cost of the AI compliance scoring on outputs that would obviously fail.
After generation: every AI output gets rechecked against the same rules before publishing. The pipeline runs the cheap regex check first, then the more expensive AI semantic check, then routes borderline cases to review.
State-by-state rules and vertical rules condition the libraries. state-specific blocklists, FDA structure-function gates, and FTC substantiation gates apply automatically based on the customer and the product. Per rule, per vertical, and per state, you choose the enforcement level: block, warn, log, or route to review.
Every pre-filter decision is captured in the audit history for regulator inquiry response. Cisco Umbrella, Fortinet, and Barracuda keep handling network filtering. Guardrails AI keeps handling general AI safety. This handles your operator-specific marketing content rule libraries.
How most operators solve this today
A few categories of tools touch this problem, but none of them load your specific marketing-content rule libraries and check every AI prompt and output:
Network content filtering (Cisco Umbrella, Fortinet FortiGuard, Barracuda Web Filter, DNSFilter, WebTitan)
$1 to $100,000+/year
Filters internet traffic at the network gate. Different category — not marketing-content pre-filter.
LLM guardrails (Guardrails AI, NVIDIA NeMo Guardrails, Lakera, Robust Intelligence)
$0 to $3,000+/month, plus enterprise
Pre-filters against general AI safety prohibitions (hate speech, harmful content, prompt injection). Not your regulatory rule libraries.
Open-source regex libraries (bad-words npm, profanity-check Python, Filter.js, PCRE, RE2)
Free open-source
You build the rule library from scratch and maintain it via your engineering team. No audit linkage for regulator defense.
Content moderation services (WebPurify, Bad Words API, ProfanityCensor, Hive, Telus / TCS / Genpact)
$10 to $50,000+/month
Built for user-generated content moderation. Not brand-produced AI content with your regulatory rules.
In-house compliance reviewer authoring regex
$60,000 to $200,000/year salary
Manual regex authoring and review. Slow iteration; per-vertical and per-state overlay maintenance is manual.
Build it in-house
Engineer + compliance reviewer time + ongoing rule maintenance
Falls apart past 1,000 content pieces a week or 3+ regulated verticals. The rule library ends up spread across spreadsheets, code, and Slack threads.
What changes when this is an agent skill
Your forbidden phrase list, approved claims library, and regulated-vertical rule libraries load into the pre-filter. Regex, blocklists, and allowlists run at two points: before the LLM call (filtering prompts that would obviously fail, saving the cost of generating doomed output) and after the LLM call (rechecking output before publish).
The pipeline runs the cheap regex check first, then the more expensive AI semantic check, then routes borderline cases to review.
State-by-state rules and vertical rules condition the libraries. state-specific blocklists, FDA structure-function gates, and FTC substantiation gates apply automatically. Per rule, per vertical, and per state, you choose the enforcement level: block, warn, log, or route to review.
Every pre-filter decision is captured in the audit history for regulator inquiry response. The deterministic step is cheap and catches the obvious cases, so the AI step only runs on outputs that need real judgment.
Agents that include this skill
Skills live inside agent rentals. To get this skill in production, hire any of the agents below — context-tuning at onboarding is included in the first month.
Vertical Compliance Overlay Manager Agent
Produces and maintains per-vertical + per-jurisdiction compliance overlays every content-producing agent loads at runtime.
FAQ
- What does this actually do?
- It checks every AI prompt and every AI output against your forbidden phrases, approved claims, and regulated-vertical rules using cheap deterministic regex. Obvious violations get blocked or routed to review before any expensive AI compliance check runs.
- How is this different from Cisco Umbrella or Fortinet?
- Network content filtering controls what employees access on the internet at the IT-managed network gate. This is a different category — it checks AI marketing content prompts and outputs against your regulatory rules.
- How is this different from Guardrails AI or NVIDIA NeMo Guardrails?
- LLM guardrails block general AI safety violations (hate speech, harmful content, prompt injection). This blocks your specific regulatory violations (FDA structure-function, FTC substantiation, FINRA suitability).
- How is this different from WebPurify or Hive?
- Those moderate user-generated content (forums, social, gaming). This pre-filters brand-produced AI marketing content against your specific regulatory rule libraries.
- What rule libraries does it enforce?
- Your forbidden phrase list, your approved claims library, and your regulated-vertical rule libraries — composed with the state-by-state and vertical overlays that apply.
- How does this work alongside the AI semantic compliance check?
- A pipeline: cheap regex first, then expensive AI semantic check on what passes, then review queue for borderline cases. The regex catches obvious cases for almost no cost, so the AI step only runs on outputs that need real judgment.
- What enforcement levels are available?
- Block (auto-reject), warn (flag but pass), log (silent record), route (send to review). You set the level per rule, per vertical, and per state.
- How does this work with state-by-state and per-vertical rules?
- Per-vertical templates provide starter rule libraries. State-by-state overlays layer on top. This enforces the composed library at output time.