Sumo Logic Mobot

Name update

We are renaming Copilot to Mobot across the product and docs. During this transition, some UI labels and screenshots may still show "Copilot". Functionality is the same. We will update names and images as the rollout completes.

Sumo Logic Mobot (formerly known as Copilot) is our AI-powered assistant that accelerates investigations and troubleshooting in logs by allowing you to ask questions in plain English and get contextual suggestions, helping first responders get to answers faster.

With its intuitive interface, Mobot automatically generates log searches from natural language queries, helping you quickly investigate performance issues, anomalies, and security threats. It also guides you through investigations step-by-step with AI-derived suggestions to refine your results for faster, more accurate resolutions. Overall, Mobot enhances incident resolution with expert level insights.

info

If you prefer not to use Mobot, you can opt out by contacting Support.

Micro Lesson: Introduction to Mobot

This short video introduces Mobot (formerly known as Copilot) and how it can help you with log search and analysis—perfect for getting a quick overview before diving in.

Key features

Mobot accelerates incident response by combining prebuilt contextual insights with natural language queries and enhancing time to insights for users across your organization. With sub-3-second response times with over 90% translation accuracy for most queries, Mobot ensures fast and dependable results for supported log sources.

• Natural language queries. Ask questions in plain English.
• Contextual suggestions. Get suggestions relevant to your troubleshooting and investigations context.
• Conversation history. Save and resume troubleshooting or investigation sessions without losing context.
• Auto-visualize. Mobot automatically generates charts from search results, which you can add directly to dashboards, reducing time and effort in data interpretation.
• Log compatibility. Mobot supports structured logs, semi-structured logs (partial JSON), and unstructured logs (e.g., Palo Alto Firewall) when Field Extraction Rules (FERs) are applied. This ensures valuable insights across a variety of log formats.
• Enhanced query experience. Auto-complete to streamline natural language queries.

Security and compliance

Sumo Logic Mobot leverages foundational models provided by Amazon Bedrock, inheriting their robust compliance and security posture. For detailed information, refer to the following Amazon Bedrock security and compliance resources:

• Security in Amazon Bedrock
• Amazon Bedrock Security and Privacy

Additionally, all aspects of our service, including Mobot, adhere to the security and compliance requirements outlined in our service agreement or in individually negotiated contracts.

• Customer data privacy. Mobot ensures customer data remains private and secure. No customer data or PII is used to train the AI models. Context for AI processing is limited to schema and field samples, reviewed for legal and compliance purposes.
• Rolling data expiration. Some features may store query history temporarily for performance, but data is expired on a rolling basis.
• AI provider. Mobot uses a foundation model served by Amazon Bedrock. The provider has no access to your data.

Who benefits from Mobot?

Mobot is ideal for users of all skill levels:

• On-call engineers. Accelerate time to resolution by surfacing key troubleshooting insights.
• Security engineers. Obtain security insights rapidly for faster security incident resolution.
• Early career professionals. Simplifies troubleshooting with natural language queries, making incident resolution accessible to those unfamiliar with query syntax.
• Practitioners. Speeds up workflows with auto-complete and context-aware suggestions for frequent tasks.
• Experts. Provides IDE-style assistance for crafting complex queries efficiently.

How to use Mobot

In this section, you'll learn the recommended workflow for using Mobot effectively, along with best practices to maximize its benefits.

Micro Lesson: Using Mobot

See Mobot (formerly known as Copilot) in action with a hands-on walkthrough of the UI and prompt-based search.

Step 1: Open Mobot

To start using Mobot:

From the New UI, click Copilot (or Mobot) in the left nav.

From the Classic UI, click the Copilot (or Mobot) tab.

Step 2: Review and adjust the auto-selected source

Mobot automatically selects a source category based on its assessment of user intent. Review the selection and adjust it if needed. You can also manually enter a source expression to define the scope of your exploration.

For example, to explore AWS WAF logs, select the appropriate source. For indexes, use _index=<index name>. Autocompletion is supported—start typing a few words to see source suggestions and choose one.

Step 3: Execute a query

Click a suggestion

Click on any of the prebuilt Suggestions prompts to launch your investigation. These AI-curated natural language insights are tailored to the specific source you've chosen.

In this example, we'll click Count the number of log entries by the collector ID. This translates the insight to a log query and renders results.

You can pin a suggestion for easy access later. Just hover over a suggestion and click Pin suggestion (pin icon). The pinned suggestion will stay at the top of your Suggestions list within that conversation.

Ask a question

In the Ask Something... field, you can manually enter a natural language prompt, similar to the prebuilt options under Suggestions. You can also use autocompletion—start typing a keyword to see relevant suggestions.

To get the best results, focus your queries on a specific, well-defined problem. Broad or vague questions may lead to inaccurate or incomplete results. If Mobot cannot translate your prompt into a valid query, you'll see a "Failed translation" message.

Whenever possible, break down complex questions into smaller, clear requirements. This helps Mobot generate more accurate and actionable responses.

Tips and tricks

• Start with a broad query. Begin with a query like Show me the most recent logs to understand the structure and available fields in your logs.
• Disambiguate field names. If fields have similar names and cause confusion, explicitly specify the field (e.g., <field_name>) to improve accuracy.
• Experiment with phrasing. Try multiple variations of a query to provide context and receive more relevant suggestions.
• Include time or variations to add timeslice as a dimension. When timeslicing data, include the term time in your query. For example: Count requests, every 1m, different code challenges and user used during login attempts by time.
• Explore context-aware suggestions. Use prompts like Calculate 95th percentile latency or Visualize request volumes over time to quickly surface key metrics.
• Detect malicious activity. Try queries like Count register requests by 503 status code, IP, and threat confidence to uncover potential DDoS attacks.

Below are examples of how you can phrase queries if the autocompletions and contextual suggestions are not relevant to you:

• Count logs by [field(s)] and Group logs by [field(s)] produce the same result
• Sort by [field(s)] [in descending order]
• Percentage by [field] values
• Find [stat] for [field] (max, min, standard deviation, etc.)
• Filter by [field] contains [keyword]
  note

  Keyword searches are case-sensitive.

• Apply logreduce to logs

More examples:

• Detecting malicious activity:

  Count logs by action. Sort the results.
  Filter results by action contains Malicious.

• Advanced analysis with users and URLs:

  Count logs by action, url, user.
  Sort the results. Filter results by action contains Malicious.

• Root cause analysis for latency:

  Calculate 95th percentile latency by service and API.

Additional prompts can trigger more advanced activities (e.g., mapping network activity against CrowdStrike):

• Analyze risk and severity of network activity
• Identify top application categories accessed

Time range

By default, Mobot searches run with a 15-minute time range. If your search returns no results, consider expanding the time range.

1. Click the clock icon and select your desired time range from the dropdown.
   
2. Click the search button.
   

Chart type

Mobot will automatically attempt to visualize your data. For example, a query like Top ip by geo will trigger a geo lookup and display the results on a map:

The following rules are used to deduce chart type:

• If both latitude and longitude fields exist, it returns a MAP chart type.
• If there is only one field and one record, it returns an SVP chart type. Example query: (_sourceCategory=ic/linux/gcp) | count by %"_sourcename" | count
• If a sort operator is present and there are string fields, it returns a TABLE. Given that there is a sort operator, probably the user is interested in count. Query: (_sourceCategory=ic/linux/gcp) | count by %"_sourcename" | sort by _count
• If there is a _timeslice field, it returns a LINE chart type if there are numeric fields or a TABLE chart type if there are string fields.
• If there is one string field, one numeric field, and record count is less than 6, it returns a PIE chart type. Query: (_sourceCategory=ic/linux/gcp) | count by %"_sourcename".
• If there is one string field, less than 3 numeric fields, and record count is less than 20, it returns a LINE chart.
• If none of the above conditions are met, it defaults to returning a TABLE chart type.

If required, select your preferred chart type, such as Table, Bar, Column, or Line view to visualize your results. You can also click Add to Dashboard to export an AI-generated dashboard for root cause analysis.

Edit query code

You can manually edit your log search query code if needed.

1. Click in the code editor field and edit your search. New to Sumo Logic query language? Learn more in the Search Query Language guide.
   
2. When you're done, press Enter or click the search button.
   

tip

To save space, you can use the Hide Log Query icon to collapse the log query code.

Compatible Log Formats

Mobot querying is compatible with JSON logs, partial JSON logs, and unstructured logs with Field Extraction Rules. It cannot be used to query metrics or trace telemetry.

To retrieve a list of _sourceCategories with JSON data, use the following query:

_sourceCategory=* "{" "}"
| limit 10000 | logreduce keys noaggregate
| count by _sourceCategory, _schema
| where _schema != "unknown"
| sum(_count) by _sourceCategory

If your log query contains a mix of JSON and non-JSON formatting (i.e., a log file is partially JSON), you can isolate the JSON portion by adding a left curly brace ({) to the source expression to trigger Suggestions.

Edit Title

Mobot automatically updates conversation titles based on your query. You can also set a custom title by clicking the "Edit Title" (pencil) icon. This helps keep investigations organized and easier to revisit.

History

The conversation history feature saves all previous queries and suggestions, allowing you to backtrack and refine your investigation. For example, if a status code analysis yields inconclusive results, you can revisit earlier queries to explore other possibilities.

This functionality can be useful when you're working on multiple incidents at the same time. To view Mobot interactions related to an incident, click History.

There are two ways to resume a conversation:

• Click the "Resume Conversation" icon to pick up from the last query in a conversation.
  
• Click on any row in a conversation history, then click the "Open in Mobot" icon to resume from a specific query in a conversation.
  

New Conversation

To start a fresh exploration, click New Conversation. This clears your current session and allows you to begin with a clean slate.

Step 4: Open in Log Search

You can open your query in Log Search to access Sumo Logic’s full search functionality. This allows you to continue investigating, refine your query, save the search, or take action as needed.

There are two ways to do this:

• From your conversation, click the "Open in Log Search" icon.
  
• From your conversation history, hover over any row, then click the "Open in Log Search" icon.
  

Example queries

Logs for security

In the video, Mobot is used to investigate a security issue involving the potential leak of AWS CloudTrail access keys outside the organization.

The video demonstrates how to use Mobot to analyze AWS CloudTrail data, review AI-curated suggestions, refine searches using natural language prompts, and generate a dashboard for root cause analysis and sharing.

Cloud SIEM

You are a SecOps engineer who uses Cloud SIEM. You are worried about a signal in Cloud SIEM regarding malicious network activity. You want to investigate network records and be proactive. You are under pressure to complete your investigation quickly. While familiar with Sumo Logic, you do not write log queries every day and could use a little help. Fortunately, all your Cloud SIEM records are in Sumo Logic.

1. In Mobot, you type the source for Cloud SIEM network records:

   _index=sec_record_network

2. You know what you are looking for. So, you ask:

   Count logs by action. Sort the results.

   
3. As soon as you do that, you can look at the Suggestions section on the right. These suggestions are curated based on their relevance to this Cloud SIEM source. You pick a suggestion to compare results to the last hour:

   Count logs by action. Sort the results. versus the previous 1h

   Notice the system translated the suggestion to a log query and rendered results as a bar graph with no user input.
   
4. Switching to table view, you notice "Malicious” in the search results. So, you add in Filter results by action contains Malicious to the query:

   Count logs by action. Sort the results. Filter results by action contains Malicious.

   
   note

   If Malicious doesn't work, try Malicious*. Sumo Logic is case sensitive.

5. Next, you look for URLs that pertain to the malicious action:

   Count logs by action, url, user. Sort the results. Filter results by action contains Malicious.

   
6. Even though the activity was blocked, you can investigate the affected users in the endpoint records next.

To summarize, you conclude there is malicious activity originating from certain users who need to be investigated further.

Role Based Access Control

Role Based Access Control is not supported for contextual suggestions and autocompletions. It is possible for a user who is blocked by log search RBAC to view suggestions or completions for unpermitted source expressions. However, they will not be executed by the search.

Search behavior and data tier access

Mobot follows the same search behavior as standard log search and respects your account’s data configuration, whether you're on classic tiered pricing or Flex pricing.

Flex pricing

For customers on Flex pricing, all data is stored in a single intelligent layer and pricing is based on the volume of data scanned.

Tiered pricing (legacy)

If you're on classic tiered pricing, Mobot by default searches across continuous data tiers only, unless otherwise specified.

To direct Mobot to search the Infrequent tier, for example, use:

_dataTier=Infrequent

FAQ

What is Sumo Logic Mobot?

Mobot is an AI assistant integrated into the Sumo Logic Log Analytics Platform. It enables natural language queries and contextual troubleshooting, helping users extract actionable insights from logs. Mobot does not process or share your log data with any third party.

Can I use Mobot to analyze unstructured logs?

Yes, Mobot can extract relevant insights from unstructured logs, provided Field Extraction Rules (FERs) are applied. It also supports semi-structured logs (JSON + unstructured payloads).

Does Mobot save search history?

Yes, Mobot retains conversation and search history, allowing you to resume investigations with context and continuity.

What role does AI play in Mobot?

Mobot uses AI to interpret natural language queries and recommend search results or query refinements, streamlining log analysis.

What specific types of customer data or PII does the AI process? Does it filter out PII/sensitive information?

Mobot processes schema and field samples to provide context to the AI. While field values can contain PII or confidential data (for example, email addresses or IP addresses), these values are used solely to enable insights and are protected under strict compliance and security reviews.

Is customer data/PII used to train AI models?

No, customer data or PII is not used for training AI models. Mobot operates using a foundation model served via Amazon Bedrock, ensuring your data remains private and secure.

How long does the AI store customer information or PII, and when and how is it deleted?

Certain features may rely on query history stored on a rolling basis for performance optimization. Data is systematically expired to maintain privacy.

For example, our alerts feature log anomaly detection and build ML models from 60 days of logs. To accomplish this, we retrain the model once a week. In this example, each week, we add one week of new data while expiring the oldest week of data. Rolling data windows are done to avoid fetching 60 days of data for every training run.

Does Mobot use any open-source library, GenAI providers, or cloud providers?

For Generative AI, Mobot uses a foundation model served by Amazon Bedrock. Classical ML features leverage open-source Python libraries approved by Sumo Logic.

What is the type of AI being used?

Mobot is an ensemble of Generative AI (GenAI) and classical machine learning (ML) techniques. For example, classical ML is used for anomaly detection in alerts.

Is there a human in the loop for Mobot?

Yes, the on-call developer or security engineer troubleshooting an incident is the expected user. They interact with Mobot using natural language questions or through contextual suggestions.

Does a fourth party have access to Mobot customer data?

No. The foundation model provider used by Amazon Bedrock has no access to customer data.

Does Sumo Logic hold any AI-specific certifications or accreditations?

No, we do not currently hold any AI-specific certifications or accreditations.

How are reviews conducted on the Mobot model?

Each major capability added to Mobot undergoes legal, compliance, and application security reviews. These reviews coincide with new releases that expand insights or process new types of data.

How can I opt out of Mobot?

If you prefer not to use Mobot, contact our support team. Your account will be updated accordingly.

Feedback

We want your feedback! Let us know what you think by clicking the thumbs up or thumbs down icon and entering the context of your query.

You can also leave feedback on specific errors.

Additional resources

• Blogs:
  • Sumo Logic Mo Copilot: AI assistant for faster incident response and simplified troubleshooting
  • Designing Sumo Logic Mo Copilot for success
  • Differentiating Sumo Logic Mo Copilot using Amazon Bedrock
• Brief: Sumo Logic's Mo Copilot speeds up response
• Webinar: Revolutionizing Incident Management with AI: Meet Mo Copilot