Detect Spam and Impersonation Profiles with AI+ Studio
Updated
Overview
Spam and impersonation accounts can misuse your brand identity to engage with customers, pose as official support representatives, and redirect users to fraudulent or misleading experiences. With Spam Profile Detection in AI+ Studio, you can automatically identify potentially suspicious profiles based on profile attributes and message content, then use Sprinklr Rules to trigger appropriate moderation workflows.
Prerequisites
Before using Spam Profile Detection, ensure the following prerequisites are met:
AI+ Studio is enabled in your Sprinklr environment.
Spam profile detection use case has to be enabled for your environment. Contact your Sprinklr Support team, Customer Success Manager, or account representative to request access.
Step 1: Navigate to Spam Profile Detection
Open AI+ Studio.
Navigate to AI Use Cases.
Under Sprinklr Social > Message Moderation, select Spam Profile Detection.
This use case helps identify potentially spammy or impersonating profiles interacting with your brand across supported social channels.
Step 2: Create a Deployment
Click Create Deployment.
Enter a name for the deployment.
Select the AI model you would like to use.
Configure the prompts according to your moderation requirements.
Add a Final Output node to your pipeline.
Save the deployment.
Click Deploy to publish the configuration.
Once deployed, the use case can evaluate profile and message information to determine whether a profile exhibits characteristics commonly associated with spam or impersonation activity.
Step 3: Configure the Output
The Spam Profile Detection deployment returns a classification that can be stored in a custom field and consumed by Sprinklr Rule Engine.
For example, you may choose to configure categories such as:
Impersonation – No Risk
Impersonation – Low Risk
Impersonation – Medium Risk
Impersonation – High Risk
Important: These categories are examples only. You can configure the output labels and classifications based on your business requirements and moderation workflows.
For example, some organizations may prefer:
Spam
Not Spam
While others may choose more granular classifications that support automated actions, review queues, and escalation processes.
After the output is generated, store the result in a custom field so that it can be referenced within Sprinklr Rules.
Step 4: Trigger Spam Detection Using Rule Engine
After creating and deploying the use case, configure a rule to execute the deployment automatically.
Navigate to Rule Engine.
Create or edit an Inbound, Queue, or Workflow Update rule.
Configure the conditions that should trigger profile evaluation.
Add the action Trigger AI+ Use Case.
Select Profile as the entity to evaluate.
Choose Spam Profile Detection as the AI+ Use Case.
Select the custom field where the AI output should be stored.
Save and activate the rule.
When the rule is triggered, Sprinklr sends the relevant profile and interaction information to the deployed Spam Profile Detection model for analysis.
Step 5: Automate Actions Based on Detection Results
Once the AI output is available in a custom field, create additional rules to automate moderation actions.
High-Risk Profiles
Examples of actions include:
Applying a spam or impersonation tag
Routing messages to a Trust & Safety queue
Assigning interactions for immediate review
Hiding comments where supported by the social network API
Medium-Risk Profiles
Examples of actions include:
Routing interactions to a moderation queue
Assigning items to reviewers for manual validation
Adding monitoring tags for further investigation
Low-Risk or No-Risk Profiles
Examples of actions include:
Allowing the interaction to remain visible
Applying monitoring or reporting tags
Taking no moderation action while retaining the classification for reporting purposes
Example Workflow
A customer comments on your brand's social media post.
A third-party profile replies claiming to represent your brand.
A Sprinklr Rule triggers the Spam Profile Detection deployment.
AI+ Studio evaluates the profile's attributes and message content.
The profile is classified according to your configured output categories.
A follow-up rule automatically routes, tags, reviews, or moderates the interaction based on the classification received.
Best Practices
Use Rule Engine for Deterministic Checks
Continue using Sprinklr Rule Engine for conditions that can be evaluated directly without AI, such as:
Message keywords
Author handle patterns
Profile verification status
Follower count thresholds
Existing custom field values
Other profile or message metadata already available in Sprinklr
Rule-based evaluations are typically faster and more cost-effective for straightforward scenarios.
Use Spam Profile Detection for Ambiguous Scenarios
Spam Profile Detection is most valuable when a decision requires contextual understanding and cannot easily be determined through simple rule conditions. Examples include:
Potential brand impersonation
Deceptive support profiles
Suspicious combinations of profile information and message content
Accounts that appear legitimate but exhibit scam-like behavior when viewed holistically
Combine Rules and AI for Best Results
Work with your Sprinklr Success team to review existing moderation workflows and identify areas where AI can complement existing rules.
A recommended approach is:
Use Rule Engine to filter obvious cases using deterministic conditions.
Invoke Spam Profile Detection only for interactions that require additional contextual analysis.
Use the AI classification output to drive moderation, review, or escalation workflows.
This hybrid approach helps optimize performance, reduce unnecessary AI usage, and improve moderation efficiency.
Start with Human Review
When deploying Spam Profile Detection for the first time, consider routing detections to review queues before enabling fully automated moderation actions. This allows your team to validate the model output and fine-tune the deployment according to your organization's requirements.