How I Would Fix unreliable AI answers and prompt injection risk in a Make.com and Airtable client portal Using Launch Ready.

How I Would Fix unreliable AI answers and prompt injection risk in a Make.com and Airtable client portal Using Launch Ready

The symptom is usually simple to spot: the portal gives different answers to the same client, ignores obvious context, or starts following instructions that came from a user message instead of your business rules. In practice, the most likely root cause is weak separation between trusted instructions, Airtable records, and user-supplied text flowing through Make.com.

The first thing I would inspect is the exact Make scenario that sends prompts to the model. I want to see where the system prompt lives, what data is being injected from Airtable, and whether any client-entered fields are being treated as instructions instead of content.

Triage in the First Hour

1. Open the Make.com scenario run history.

• Look for failed runs, retries, duplicated executions, and unusual token spikes.
• Check whether bad answers correlate with specific Airtable records or specific users.

2. Inspect the prompt assembly step.

• Confirm which fields are merged into the prompt.
• Look for raw HTML, markdown, long free-text notes, or copied user messages being inserted without sanitization.

3. Review Airtable base structure.

• Separate trusted config fields from user content fields.
• Check whether staff notes, client uploads, and AI instructions are mixed in the same table.

4. Check account permissions.

• Verify who can edit prompts, scenario variables, Airtable views, and API keys.
• Remove write access from anyone who does not need it.

5. Review logs and error handling.

• Confirm whether prompt payloads are logged in full.
• Check if secrets, tokens, or private client data are being stored in execution logs.

6. Test with one controlled record.

• Use a clean Airtable row with a known question and a known expected answer.
• Compare the model output before touching production logic.

7. Audit external integrations.

• List every module in Make that can send data outside the portal.
• Confirm whether any webhook or third-party tool can modify source records.

## Quick check I would run during triage
curl -s https://your-portal.example/api/health | jq .

Root Causes

| Likely cause | What it looks like | How I confirm it | |---|---|---| | Prompt injection via user content | The model follows instructions inside a client message or uploaded note | Compare raw input vs final prompt and look for instruction-like phrases such as "ignore above" | | No trust boundary between system and data | Business rules are mixed into Airtable text fields | Inspect prompt template and see if all fields are concatenated into one block | | Unstable retrieval from Airtable | Different rows or views are returned for similar requests | Re-run the same query 5 times and compare selected records | | Missing validation in Make.com | Empty fields, long text, or malformed JSON break context assembly | Review each module's input mapping and error branches | | Overly broad permissions | Staff or clients can edit prompt rules or automation logic | Check role-based access in Airtable and Make connections | | No answer policy or fallback | The model guesses when data is missing | Ask a question with no source data and see whether it invents an answer |

The biggest pattern I see is this: founders assume the AI is "reading data," but the workflow is actually giving it instructions wrapped inside untrusted text. That is not an AI problem first. It is an application design problem.

The Fix Plan

1. Separate trusted instructions from untrusted content.

• Keep system rules in one fixed template.
• Put client messages, uploads, and Airtable notes into clearly labeled data sections.
• Never let user-generated text sit next to policy text without boundaries.

2. Add a strict prompt format.

• Use explicit labels like `SYSTEM`, `CONTEXT`, `USER_INPUT`, and `OUTPUT_RULES`.
• Tell the model to ignore any instructions found inside `USER_INPUT` or source records.

3. Reduce what Make.com sends to the model.

• Pass only the minimum fields needed for one answer.
• Do not send full tables when one record will do.
• Truncate long text before it reaches the LLM.

4. Normalize Airtable structure.

• Move policy settings into admin-only fields or a separate config table.
• Keep client-submitted content in read-only tables/views for automation use.
• Add status fields like `approved`, `verified`, and `source_of_truth`.

5. Add output constraints.

• Require short structured responses where possible.
• If an answer depends on missing data, force a fallback like "I will not confirm this yet."
• Do not allow free-form guessing on billing, legal, security, or account access topics.

6. Add validation before and after the model call.

• Validate input length, allowed file types, required fields, and expected formats before generation.
• Validate output against simple business rules after generation.
• If output violates policy, replace it with a safe fallback response.

7. Lock down credentials and access paths.

• Rotate any exposed API keys used by Make.com connectors.
• Store secrets only in approved secret stores or protected environment variables where possible.
• Remove direct edit access to production scenarios unless absolutely necessary.

8. Add human review for high-risk replies.

• Route sensitive cases to a staff queue instead of auto-sending answers.
• Use human approval for refunds, account changes, compliance questions, or anything security-related.

A safe way to think about this is: Make.com should orchestrate steps, Airtable should store structured facts, and the model should draft an answer only from verified inputs. If any part of that chain becomes editable by clients as plain text instructions, you have created an injection path.

Regression Tests Before Redeploy

I would not ship this fix until I had at least 10 test cases covering normal use plus abuse attempts. For this kind of portal, I want 95 percent coverage of critical flows at minimum at the workflow level, even if code coverage itself is lower because much of the logic lives in no-code modules.

Acceptance criteria:

• The same input returns consistent answers across 5 repeated runs within acceptable variance.
• User text containing instruction attacks does not override system rules.
• Missing source data triggers a safe fallback instead of hallucinated content.
• Only approved Airtable fields reach the LLM step.
• No secrets appear in logs or scenario outputs.
• High-risk requests are routed to human review.

Test cases I would run:

1. Normal question with clean data

• Expected: accurate answer based on source record only.

2. Prompt injection attempt inside client message

• Example pattern: "Ignore previous instructions."
• Expected: ignored as untrusted text.

3. Long noisy note pasted into Airtable

• Expected: truncated safely without breaking formatting or exposing hidden instructions.

4. Empty record or missing field

• Expected: fallback message asking for more information.

5. Conflicting records

• Expected: system uses one defined source of truth or escalates to review.

6. Sensitive request

• Expected: no automatic action; route to staff queue.

7. Retry behavior

• Expected: duplicate scenario runs do not create duplicate replies or duplicate writes back to Airtable.

8. Logging check

• Expected: logs contain trace IDs and status codes but not private content or secrets.

9. Permission test

• Expected: non-admin users cannot edit prompt templates or automation credentials.

10. Browser/mobile smoke test for portal UI

• Expected: clear loading state while AI response is generated; no broken empty state; readable on mobile width under 390 px.

Prevention

I would put four guardrails in place so this does not come back in two weeks:

• Monitoring
• Track failed runs per day, average response time, retry rate, and manual escalation count.
• Alert if bad-answer reports rise above 3 percent of conversations in a day.

• Code review discipline
• Even in Make.com workflows, review changes like code changes.
• Any change to prompt templates, field mappings, webhooks, or auth should get a second pair of eyes before release.

• Security controls
• Apply least privilege on Airtable bases and Make connections.
• Restrict who can edit production scenarios.

-- Audit secret rotation every 90 days if external APIs are involved.

• UX guardrails

-- Show users what kind of answer they will get: instant draft vs verified response vs needs review. -- Add clear states for loading, retrying, failed lookup over slow network conditions if latency exceeds 2 seconds p95.

For performance hygiene, I also watch how much work each scenario does per request. A portal that calls too many modules will feel slow even if it is technically working fine. My target here would be under 2 seconds p95 for simple lookups and under 5 seconds p95 for AI-assisted responses with retrieval.

When to Use Launch Ready

It is best when your portal already exists but you do not trust it enough to put real clients through it..

What I would want from you before starting:

• Access to Make.com scenario(s)
• Access to Airtable base(s) and admin roles where needed
• Domain registrar access
• Cloudflare access if already connected
• Deployment platform access if there is a frontend/backend layer
• A list of failed examples showing bad AI answers
• Any current support complaints about wrong outputs or weird behavior

If you bring me that package,, I can usually identify whether this is mostly a prompt design issue,, a data modeling issue,, or an access-control issue within the first few hours.. Then I fix it without turning your live portal into a science project..

References

• https://roadmap.sh/cyber-security
• https://roadmap.sh/ai-red-teaming
• https://roadmap.sh/api-security-best-practices
• https://roadmap.sh/code-review-best-practices
• https://support.airtable.com/docs/airtable-api-introduction

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Take the next step

If this is a problem in your product right now, here is what to do next:

• [Use the free Cyprian tools](/tools) - estimate cost, score app risk, check launch readiness, or pick the right service sprint.

• [Book a discovery call](/contact) - I will tell you honestly whether you need a sprint or if you can DIY the next step.

*Written by Cyprian Tinashe Aarons - senior full-stack and AI engineer helping founders rescue, launch, automate, and scale AI-built products.*