> ## Documentation Index
> Fetch the complete documentation index at: https://docs.endorlabs.com/llms.txt
> Use this file to discover all available pages before exploring further.

# AI model findings

> Find and manage priority issues related to AI models.

export const YamlTable = ({children, data: propData, content}) => {
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      <path d="M13.9 21.818a10 10 0 0 1-3.8 0" />
      <path d="M17.609 3.721a10 10 0 0 1 2.69 2.7" />
      <path d="M2.182 13.9a10 10 0 0 1 0-3.8" />
      <path d="M20.279 17.609a10 10 0 0 1-2.7 2.69" />
      <path d="M21.818 10.1a10 10 0 0 1 0 3.8" />
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      <path d="M6.391 20.279a10 10 0 0 1-2.69-2.7" />
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      <circle cx="12" cy="12" r="10" />
      <path d="M8 12h8" />
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    label: 'Success',
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    label: 'Failure',
    render: renderFailureIcon
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    re: /^-status-partial-success-$/i,
    colorClass: 'yt-status-color-partial',
    label: 'Partial success',
    render: renderPartialSuccessIcon
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    re: /^-status-pending-$/i,
    colorClass: 'yt-status-color-neutral',
    label: 'Pending',
    render: renderPendingIcon
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    re: /^-status-running-$/i,
    colorClass: 'yt-status-color-neutral',
    label: 'Running',
    render: renderRunningIcon
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    re: /^-status-skipped-$/i,
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    if (LIMITED_RE.test(text)) return <span className="yt-badge-limited" role="img" aria-label="Partially supported" title="Partially supported">◐</span>;
    if (NA_RE.test(text) || NA2_RE.test(text)) return <span className="yt-sr-only" title="Not applicable">Not applicable</span>;
    const statusBadge = STATUS_ICON_DEFS.find(def => def.re.test(text));
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    return processText(text);
  };
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    if (NA2_RE.test(text)) return 'yt-cell-na2';
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  if (!mounted) return null;
  if (!data || data.length === 0) return null;
  const rowKey = row => columns.map(c => row[c] || '').join('|');
  return <div ref={scrollWrapRef} style={{
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    margin: '1.75rem 0'
  }} role={isScrollable ? 'region' : undefined} aria-label={isScrollable ? 'Scrollable table' : undefined} tabIndex={isScrollable ? 0 : undefined}>
      <table style={{
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    minWidth: '100%',
    margin: 0
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          </tr>
        </thead>
        <tbody>
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              {columns.map(col => <td key={col} className={cellClassName(row[col])}>{processBadges(row[col])}</td>)}
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};

Endor Labs can detect AI models and list them as dependencies when you run a scan with the `--ai-models` flag. You can view the detected AI models in the **Inventory** > **AI Models** section of the [Endor Labs user interface](#view-ai-models-in-your-namespace).

You can define custom policies to flag the usage of specific AI providers, specific AI models, or models with low scores so that their usage raises findings as part of your scan. Endor Labs provides [AI model policy templates](/secure-ai-coding/ai-model-discovery/ai-model-policies) that you can use to create finding policies that are tailored to your organization's needs. You can [view these findings](#view-ai-model-findings-in-your-namespace) in **Code Dependencies** > **AI Models** on the **Findings** page.

Run the following command to detect AI models in your repository.

```bash theme={null}
endorctl scan --ai-models
```

When you run a scan with the `--ai-models` option, Endor Labs downloads Opengrep and runs Opengrep to detect AI models.

Endor Labs detects AI models using pattern matching and can use LLM processing to improve detection accuracy. LLM processing is disabled by default.

See [Supported AI model providers](#ai-model-detection) for the list of external AI models detected by Endor Labs. Only Hugging Face models are scored, as they are open source and provide extensive public metadata. Models from all other providers are detected but not scored due to limited metadata.

## Enable LLM processing for AI model detection

To enable LLM processing in Endor Labs:

1. Select **User menu** > **Settings** from the left sidebar.
2. Select **System settings** > **Data privacy**.
3. Turn on **Code Segment Embeddings and LLM Processing**.

See [Configure system settings](/platform-administration/configure-system-settings) for more information.

<Note>
  **Privacy**

  When you enable LLM processing, Endor Labs uses a private and isolated Azure OpenAI Service deployment, which is not accessible from the public Internet and cannot be used for LLM training.
</Note>

To generate AI model findings:

1. Configure [finding policy](/secure-ai-coding/ai-model-discovery/ai-model-policies) to detect AI models with low scores and enforce organizational restrictions on specific AI models or model providers.

2. [View AI Model findings](#view-ai-model-findings-in-your-namespace).

3. To disable AI model discovery, set `ENDOR_SCAN_AI_MODELS=false` in your [scan profile](/scan/scan-profiles/configure-scanprofile-ui#configure-general-scan-profile-settings).

## AI model detection

The following table lists the AI model providers currently supported by Endor Labs for model detection. For each provider, the table includes supported programming languages, if model scoring is available, and a reference link to the provider's API documentation.

<YamlTable>
  {`


    - AI_model: HuggingFace
    Supported_languages: Python
    Endor_score: -yes-
    Reference: <https://huggingface.co/docs>

    - AI_model: OpenAI
    Supported_languages: Python, JavaScript, Java (beta), Go (beta), C#
    Endor_score: -no-
    Reference: <https://platform.openai.com/docs/libraries>

    - AI_model: Anthropic
    Supported_languages: Python, TypeScript, JavaScript, Java (alpha), Go (alpha)
    Endor_score: -no-
    Reference: <https://docs.anthropic.com/en/api/client-sdks>

    - AI_model: Google
    Supported_languages: Python, JavaScript, TypeScript, Go
    Endor_score: -no-
    Reference: <https://ai.google.dev/gemini-api/docs/sdks>

    - AI_model: AWS
    Supported_languages: Python, JavaScript, Java, Go, C#, PHP, Ruby
    Endor_score: -no-
    Reference: <https://docs.aws.amazon.com/bedrock/latest/APIReference/welcome.html#sdk>

    - AI_model: Perplexity
    Supported_languages: Python
    Endor_score: -no-
    Reference: <https://docs.perplexity.ai/api-reference/chat-completions-post>

    - AI_model: DeepSeek
    Supported_languages: Python, JavaScript, Go, PHP, Ruby
    Endor_score: -no-
    Reference: <https://api-docs.deepseek.com/api/deepseek-api>

    - AI_model: Azure OpenAI
    Supported_languages: C#, Go, Java, Python
    Endor_score: -no-
    Reference: <https://learn.microsoft.com/en-us/azure/ai-foundry/>


    `}
</YamlTable>

## AI model discovery through monitoring scans

By default, AI models are discovered during SCA scans run through GitHub App, Bitbucket App, Azure DevOps App, and GitLab App. You can view the reported AI models under **Inventory** > **AI Models** in the left sidebar.

To disable AI model discovery, set `ENDOR_SCAN_AI_MODELS=false` as an additional environment variable in the [scan profile](/scan/scan-profiles/configure-scanprofile-ui#configure-general-scan-profile-settings) and assign the scan profile to the project.

## Detect AI models

Configure finding policies and perform an endorctl scan to detect AI models in your repositories and review the findings.

1. Configure [finding policy](/secure-ai-coding/ai-model-discovery/ai-model-policies) to detect AI models with low scores and enforce organizational restrictions on specific AI models or model providers.

2. Run an endorctl scan with the following command.

   ```bash theme={null}
   endorctl scan --ai-models --dependencies
   ```

## View AI models in your namespace

To view all AI models that are used in your namespace:

1. Select **Inventory** > **AI Models** from the left sidebar.
   <img src="https://mintcdn.com/endorlabs-b4795f4f/Uc3T4mPoaFbRUPbf/images/secure-ai-coding/ai-model-discovery/ai-inventory-ui.webp?fit=max&auto=format&n=Uc3T4mPoaFbRUPbf&q=85&s=61af7f3217b9514b200763240ef4f740" alt="AI Models under Inventory" style={{width: '60%'}} width="1800" height="1108" data-path="images/secure-ai-coding/ai-model-discovery/ai-inventory-ui.webp" />
2. Use the search bar to look for any specific models.
3. Select a model, and click to see its details.
4. You can also navigate to **Findings** > **AI Models** from the left sidebar to view AI model findings.

   <img src="https://mintcdn.com/endorlabs-b4795f4f/Uc3T4mPoaFbRUPbf/images/secure-ai-coding/ai-model-discovery/aimodel-global-finding.webp?fit=max&auto=format&n=Uc3T4mPoaFbRUPbf&q=85&s=0aca2c936421f5bc80cb65c457e97a3f" alt="AI model findings" style={{width: '70%'}} width="2284" height="1174" data-path="images/secure-ai-coding/ai-model-discovery/aimodel-global-finding.webp" />

## View AI models in a project

To view AI models that are used in a specific project:

1. Select **Projects** from the left sidebar and select a project.
2. Select **Inventory** and click **AI Models** under **Dependencies** to view findings.
   <img src="https://mintcdn.com/endorlabs-b4795f4f/Uc3T4mPoaFbRUPbf/images/secure-ai-coding/ai-model-discovery/aimodel-dependency.webp?fit=max&auto=format&n=Uc3T4mPoaFbRUPbf&q=85&s=3f7dcf31e4d0a1535aa0f38273ac0c17" alt="AI model dependencies" style={{width: '70%'}} width="2284" height="676" data-path="images/secure-ai-coding/ai-model-discovery/aimodel-dependency.webp" />


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