How to Build a Spam Honey Trap in HighLevel Using Workflows and AI

If you connect all your communication channels to HighLevel, there is an inevitable consequence.

The genuine messages come into HighLevel. But so does the spam.

Email, SMS, WhatsApp, Facebook Messenger and Instagram messages can all end up in your CRM.

And the problem isn’t simply having to look at annoying spam messages.

Those incoming messages can create spam contacts in your HighLevel database.

That potentially creates two much bigger problems.

1. Spam Contacts Can Fill Up Your CRM

Every spam submission that becomes a contact is another record sitting in your CRM.

If your CRM provider charges according to the number of contacts you store, allowing thousands of spam contacts to accumulate can potentially push you into higher contact tiers and increase your costs.

HighLevel itself is relatively unusual in offering unlimited contacts on its main agency plans, but this is still an important consideration when designing CRM systems generally, particularly if data is being synchronised with other platforms that do charge by contact volume.

Even when there isn’t a direct additional contact cost, you probably don’t want thousands of fake Facebook accounts, spammers and bots mixed in with your genuine prospects and customers.

2. Spam Contacts Can Become a Deliverability Problem

This is potentially the more serious issue.

Imagine a spam email message creates a new contact in your CRM.

Six months later, you send a marketing campaign to everyone in your database.

You’ve now potentially taken a contact that sent spam to you and turned it into a contact that you are trying to send marketing messages to.

That’s not good.

The same principle can apply across SMS and WhatsApp.

If your CRM repeatedly attempts to push outbound messages to fake, invalid or unwanted contacts, you risk poor delivery signals, bounces, complaints and other negative engagement.

Over time, that can contribute to problems with your sender reputation and messaging deliverability.

So the objective isn’t simply:

“How do we stop our team seeing annoying spam?”

It is:

“How do we identify spam contacts as they enter HighLevel and prevent them from contaminating our genuine marketing database?”

Back in my Infusionsoft days, we used to build automations that we called “Honey Traps”.

The idea was simple. Catch contacts that clearly weren’t genuine prospects or customers before they became mixed into the rest of the database.

With HighLevel, we can take that old Honey Trap idea considerably further.

We can combine simple workflow rules with AI to create a multi-stage Spam Honey Trap.

And the clever bit is that we don’t need to pay for AI to analyse every incoming message.

We can use simple workflow logic to catch the obvious spam first. Then only messages that get through that first layer need the more sophisticated AI treatment.

That’s the basis of the HighLevel Spam Honey Trap we’re going to build.

How the Multi-Stage Spam Honey Trap HighLevel Workflow Works

At its simplest, the process looks like this:

The important point is that AI isn’t our first line of defence.

There’s little point paying AI to analyse a message if a simple HighLevel workflow condition could have identified it immediately.

Stage 1: Catch the Obvious Spam With If/Else Logic

We can start our workflow using the Customer Replied trigger.

HighLevel allows this trigger to respond to incoming messages and filter them according to things such as the reply channel and phrases contained in the message.

That gives us our first Honey Trap.

Think about the spam you already receive.

You may repeatedly see messages containing phrases such as:

• “Your Facebook page will be deleted”
• “Your account has been suspended”
• “Community standards violation”
• “Copyright infringement”
• “Your account will be permanently disabled”
• Other recurring phrases specific to the spam targeting your business

HighLevel’s own spam example demonstrates this approach using words and phrases such as “violation”, “permission” and “suspension”.

These are known patterns.

We don’t need AI to understand them.

We can simply use workflow logic to ask:

Does the incoming message contain one of our known spam phrases?

YES → Send it down the Spam route.

NO → Move to Stage 2.

HighLevel’s Customer Replied trigger currently supports Contains Phrase and Exact Match Phrase filters, as well as filtering according to tags, intent and reply channel.

That means you could also have slightly different Honey Traps depending on whether the message arrived by email, SMS, WhatsApp or social messaging.

Don’t Try to Catch Everything With Keywords

This is important.

The Stage 1 Honey Trap isn’t supposed to be perfect.

If you build hundreds of complicated rules trying to identify every possible piece of spam, you could create something that’s difficult to maintain and potentially catches legitimate messages.

A genuine customer could quite reasonably use the word “suspended” or “violation”.

So Stage 1 should concentrate on the really obvious patterns.

If we’re not confident, let the message continue.

That’s what Stage 2 is for.

Stage 2: Let AI Look at the Message

Now we reach the messages that weren’t obvious enough for our simple rules.

This is where AI becomes useful.

Rather than looking for individual words, AI can consider the meaning and context of the entire message.

We could instruct it along the lines of:

We can also give the AI examples.

Spam:
“Your Facebook Business Page has violated our policies and will be permanently deleted. Click here to appeal.”

Legitimate:
“I tried to log into my account this morning but it says my account has been suspended. Can somebody help?”

Both contain language about accounts being suspended.

A simple keyword rule could struggle with that.

AI has a much better chance of understanding the difference.

HighLevel now specifically documents spam submission detection as an example use case for its AI Decision Maker. The AI can be given form or contact data, examples of spam, and instructions for routing the contact into the appropriate branch.

This AI step is a premium action, which makes our Stage 1 filtering even more worthwhile.

Why pay for an AI decision when a simple rule could have dealt with the message first?

Don’t Just Ask AI “Spam or Not Spam?”

I’d also avoid making this a simple binary decision.

Instead, I’d create classifications such as:

• LEGITIMATE
• LIKELY SPAM
• DEFINITE SPAM
• UNSURE

This gives us much more control over what happens next.

LEGITIMATE
Continue normally.

DEFINITE SPAM
Tag and quarantine.

LIKELY SPAM
Tag as suspected spam and potentially send for review.

UNSURE
Send to a human.

That last option is important.

AI doesn’t have to make every decision.

Sometimes the best decision AI can make is:

“I’m not sufficiently confident. A person needs to look at this.”

Stage 3: Create a Spam Quarantine

I wouldn’t start by automatically deleting everything the AI identifies as spam.

Initially apply a spam tag, run the automation for a week or two and inspect the contacts being identified. Only once you’re confident in the results should you consider something more aggressive such as automatic deletion.

I like to think of this as creating a Spam Quarantine.

For example:

Tag: Suspected Spam

You could potentially also record:

• Spam classification
• Reason for classification
• Channel
• Date detected

Most importantly, these contacts can then be excluded from the normal processes intended for genuine prospects and customers.

That could mean keeping them out of sales pipelines, marketing campaigns and automated follow-up.

This is particularly important because it addresses one of the problems we discussed at the beginning.

We don’t want a spammer who contacted us today accidentally receiving our email, SMS or WhatsApp marketing six months from now.

Stage 4: Build a Spam Library

This is where I think the Honey Trap becomes much more interesting.

Instead of trying to predict every type of spam in advance, we can build a library from the actual spam our business receives.

And we can make it incredibly simple for the team to maintain.

Create a Google Sheet called something like:

HighLevel Spam Library

Then something interesting happens.

HighLevel can now connect Google Sheets directly to a Knowledge Base.

The Sheet becomes a live source of information that HighLevel’s AI tools can reference.

Google Sheet Knowledge Base sources can also automatically synchronise, so changes made by the team can be reflected in the Knowledge Base without continually exporting and uploading files.

Let Your Team Teach the Spam Honey Trap

Imagine the following situation.

A completely new spam message arrives.

Stage 1 doesn’t catch it because we’ve never seen the wording before.

AI isn’t confident enough to classify it as definite spam.

So it reaches a member of your customer service team.

They immediately recognise it as spam.

Normally they might simply delete it.

But now they do something else.

They add it to the Spam Library.

The Google Sheet updates.

The Knowledge Base updates.

The next time the AI encounters something similar, it has another real example to work from.

This gives us a potentially very useful feedback loop:

We’re effectively allowing the people dealing with the messages every day to help improve the Honey Trap.

And they don’t need to understand HighLevel workflows or AI prompts to do it.

They just need to add another row to a Google Sheet.

Don’t Just Store Spam Examples

I’d actually take this one stage further.

The library could contain examples of legitimate messages too.

Why?

Because we’re not merely trying to teach AI:

“This looks like spam.”

We’re also teaching it:

“This might look suspicious, but it’s actually genuine.”

Imagine you run an IT company.

A genuine customer might send:

“My Microsoft account has been suspended and I’ve been told there has been a security violation. Can you help?”

Those words could look remarkably similar to a phishing message.

By maintaining examples of both spam and legitimate communications, we’re giving AI more context on which to base its decision.

The Clever Bit: Make the Spam Honey Trap Cheaper Over Time

Here’s another aspect I particularly like.

Suppose our AI identifies 30 messages over a few weeks that are all essentially the same scam.

We inspect them and discover they all contain a distinctive phrase.

We now promote that pattern into Stage 1.

Instead of paying AI to analyse message number 31, our free If/Else rule catches it.

So the Honey Trap potentially becomes:

• Smarter.
• More accurate.
• And cheaper to operate.

AI deals with the unusual cases.

Traditional automation gradually takes over the repetitive cases.

That’s exactly how I think AI and conventional automation should work together.

Use Rules for What You Know. AI for What You Don’t.

There is currently a temptation to throw AI at every automation problem.

I don’t think that’s always the right answer.

If we know that a particular phrase almost certainly indicates spam, an If/Else condition is quicker, predictable and essentially free.

We don’t need AI.

But if the decision requires understanding context, intent and language, that’s exactly where AI starts to earn its keep.

That’s a much more interesting use of AI than simply replacing an If/Else condition with ChatGPT.

This Can Work Across Multiple HighLevel Communication Channels

Another advantage of building the Honey Trap inside HighLevel is that we’re not simply building an email spam filter.

HighLevel’s Customer Replied workflow trigger can distinguish between reply channels.

That means the same overall idea can potentially be applied differently according to where the message originated.

You might discover that:

Facebook and Instagram attract fake Meta account warnings.

Email attracts unsolicited SEO, web design and lead generation pitches.

SMS attracts another type of unwanted message.

WhatsApp develops its own spam patterns.

The underlying architecture remains the same.

Cheap rules first. AI second. Human review when necessary.

But the rules and examples can be tailored to the channel.

What About Spam Form Submissions?

There’s another variation worth considering.

Not all spam arrives as a reply.

Website forms can attract automated bots and junk submissions too.

This is particularly relevant if every form submission automatically:

• Creates a contact
• Creates an opportunity
• Notifies a salesperson
• Starts a nurture workflow
• Adds the person to future marketing

The same multi-stage philosophy can be used here.

Known junk patterns can be filtered first.

Anything less obvious can be assessed by AI before an opportunity is created or the contact is pushed further into your sales process.

HighLevel itself now uses spam form submissions as an example of what its AI Decision Maker can classify.

So the Honey Trap doesn’t necessarily have to protect only your Conversations inbox.

It can sit at several of the entrances into your CRM.

Start Conservatively

If I were implementing this for a business, I wouldn’t turn everything on and immediately start deleting contacts.

I’d build it gradually.

1. Start with your most obvious spam phrases.
2. Run those through Stage 1.
3. Then introduce AI.
4. Tag suspected spam rather than deleting it.
5. Review what the system catches.
6. Look carefully at the false positives.
7. Add new examples to your Spam Library.
8. Then gradually improve the rules.

Once you’ve seen the system working accurately for a reasonable period, you can decide how aggressively you want it to deal with definite spam.

The objective isn’t to create the world’s cleverest spam filter on day one.

It’s to create something safe that improves with experience.

From Infusionsoft Spam Honey Traps to AI Honey Traps

I’ve been building CRM automations for a long time.

Back in my Infusionsoft days, our Honey Traps were largely based on rules.

If X happens, do Y.

Those rules are still incredibly useful.

But today we can add another layer.

HighLevel gives us workflows capable of reacting to incoming communications across different channels. We can combine those traditional rules with AI that can actually examine the content and context of a message.

Then we can add human experience back into the loop through something as simple as a Google Sheet.

That creates a very different type of Honey Trap.

• Rules catch what we already recognise.
• AI investigates what isn’t obvious.
• Humans deal with the exceptions.
• The Spam Library captures what the humans learn.

And when a new spam pattern becomes predictable, we can move it back into the free rule-based layer.

It’s a simple idea.

But it’s also a good example of how I think AI should be used inside HighLevel.

HighLevel Help Doc on AI Decision Maker.