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Amazon Bedrock
Version 1.2.0 (View all)
Compatible Kibana version(s) 8.16.5 or higher
9.0.0 or higher
Supported Serverless project types
What's this?		 Security
Observability
Subscription level
What's this?		 Basic
Level of support
What's this?		 Elastic

Amazon Bedrock offers a fully managed service that provides access to high-performing foundation models (FMs) from leading AI startups and Amazon through a unified API. You can choose from a wide variety of foundation models to find the one that best fits your specific use case. With Amazon Bedrock, you gain access to robust tools for building generative AI applications with security, privacy, and responsible AI practices. Amazon Bedrock enables you to easily experiment with and evaluate top foundation models, customize them privately with your data using methods like fine-tuning and Retrieval Augmented Generation (RAG), and develop agents that perform tasks by leveraging your enterprise systems and data sources.

The Amazon Bedrock integration enables a seamless connection of your model to Elastic to efficiently collect and monitor invocation logs and runtime metrics.

Elastic Security can leverage this data for security analytics including correlation, visualization and incident response. With invocation logging, you can collect the full request and response data, and any metadata associated with use of your account.

Important

Extra AWS charges on API requests will be generated by this integration. Check API Requests for more details.

This integration is compatible with the Amazon Bedrock ModelInvocationLog schema, version 1.0.

The Amazon Bedrock integration collects model invocation logs and runtime metrics.

Data streams:

  • invocation: Collects invocation logs, model input data, and model output data for all invocations in your AWS account used in Amazon Bedrock.
  • runtime: Collects Amazon Bedrock runtime metrics such as model invocation count, invocation latency, input token count, output token count and many more.
  • guardrails: Collects Amazon Bedrock Guardrails metrics such as guardrail invocation count, guardrail invocation latency, text unit utilization count, guardrail policy types associated with interventions and many more.

You need Elasticsearch for storing and searching your data and Kibana for visualizing and managing it. You can use our hosted Elasticsearch Service on Elastic Cloud, which is recommended, or self-manage the Elastic Stack on your own hardware.

Before using any Amazon Bedrock integration you will need:

  • AWS Credentials to connect with your AWS account.
  • AWS Permissions to make sure the user you're using to connect has permission to share the relevant data.

For more details about these requirements, check the AWS integration documentation.

  • Elastic Agent must be installed. For detailed guidance, follow these instructions.
  • You can install only one Elastic Agent per host.
  • Elastic Agent is required to stream data from the S3 bucket and ship the data to Elastic, where the events will then be processed through the integration's ingest pipelines.

To use the Amazon Bedrock model invocation logs, the logging model invocation logging must be enabled and be sent to a log store destination, either S3 or CloudWatch. For more details, check the Amazon Bedrock User Guide.

  1. Set up an Amazon S3 or CloudWatch Logs destination.
  2. Enable logging. You can do it either through the Amazon Bedrock console or the Amazon Bedrock API.

When log collection from an S3 bucket is enabled, you can access logs from S3 objects referenced by S3 notification events received through an SQS queue or by directly polling the list of S3 objects within the bucket.

The use of SQS notification is preferred: polling list of S3 objects is expensive in terms of performance and costs and should be used only when no SQS notification can be attached to the S3 buckets. This input integration also supports S3 notification from SNS to SQS.

To enable the SQS notification method, set the queue_url configuration value. To enable the S3 bucket list polling method, configure both the bucket_arn and number_of_workers values. Note that queue_url and bucket_arn cannot be set simultaneously, and at least one of these values must be specified.

When CloudWatch log collection is enabled, you can retrieve logs from all log streams within a specified log group. The filterLogEvents AWS API is used to list log events from the specified log group.

Exported fields
Field Description Type
@timestamp Date/time when the event originated. This is the date/time extracted from the event, typically representing when the event was generated by the source. If the event source has no original timestamp, this value is typically populated by the first time the event was received by the pipeline. Required field for all events. date
aws.cloudwatch.message CloudWatch log message. text
aws.s3.bucket.arn ARN of the S3 bucket that this log retrieved from. keyword
aws.s3.bucket.name Name of the S3 bucket that this log retrieved from. keyword
aws.s3.object.key Name of the S3 object that this log retrieved from. keyword
aws_bedrock.invocation.artifacts flattened
aws_bedrock.invocation.error keyword
aws_bedrock.invocation.error_code keyword
aws_bedrock.invocation.image_generation_config.cfg_scale double
aws_bedrock.invocation.image_generation_config.height long
aws_bedrock.invocation.image_generation_config.number_of_images long
aws_bedrock.invocation.image_generation_config.quality keyword
aws_bedrock.invocation.image_generation_config.seed long
aws_bedrock.invocation.image_generation_config.width long
aws_bedrock.invocation.image_variation_params.images keyword
aws_bedrock.invocation.image_variation_params.text keyword
aws_bedrock.invocation.images keyword
aws_bedrock.invocation.input.input_body_json flattened
aws_bedrock.invocation.input.input_body_json_massive_hash keyword
aws_bedrock.invocation.input.input_body_json_massive_length long
aws_bedrock.invocation.input.input_body_s3_path keyword
aws_bedrock.invocation.input.input_content_type keyword
aws_bedrock.invocation.input.input_token_count The number of tokens used in the GenAI input. long
aws_bedrock.invocation.input.messages_content_kinds The different content formats related to the input messages/prompts. keyword
aws_bedrock.invocation.model_id keyword
aws_bedrock.invocation.output.completion_text The formatted LLM text model responses. Only a limited number of LLM text models are supported. text
aws_bedrock.invocation.output.output_body_json flattened
aws_bedrock.invocation.output.output_body_s3_path keyword
aws_bedrock.invocation.output.output_content_type keyword
aws_bedrock.invocation.output.output_token_count long
aws_bedrock.invocation.request_id keyword
aws_bedrock.invocation.result keyword
aws_bedrock.invocation.schema_type keyword
aws_bedrock.invocation.schema_version keyword
aws_bedrock.invocation.task_type keyword
cloud.image.id Image ID for the cloud instance. keyword
data_stream.dataset The field can contain anything that makes sense to signify the source of the data. Examples include nginx.access, prometheus, endpoint etc. For data streams that otherwise fit, but that do not have dataset set we use the value "generic" for the dataset value. event.dataset should have the same value as data_stream.dataset. Beyond the Elasticsearch data stream naming criteria noted above, the dataset value has additional restrictions: * Must not contain - * No longer than 100 characters constant_keyword
data_stream.namespace A user defined namespace. Namespaces are useful to allow grouping of data. Many users already organize their indices this way, and the data stream naming scheme now provides this best practice as a default. Many users will populate this field with default. If no value is used, it falls back to default. Beyond the Elasticsearch index naming criteria noted above, namespace value has the additional restrictions: * Must not contain - * No longer than 100 characters constant_keyword
data_stream.type An overarching type for the data stream. Currently allowed values are "logs" and "metrics". We expect to also add "traces" and "synthetics" in the near future. constant_keyword
event.dataset Event dataset constant_keyword
event.module Name of the module this data is coming from. If your monitoring agent supports the concept of modules or plugins to process events of a given source (e.g. Apache logs), event.module should contain the name of this module. constant_keyword
gen_ai.analysis.action_recommended Recommended actions based on the analysis. keyword
gen_ai.analysis.findings Detailed findings from security tools. nested
gen_ai.analysis.function Name of the security or analysis function used. keyword
gen_ai.analysis.tool_names Name of the security or analysis tools used. keyword
gen_ai.completion The full text of the LLM's response. text
gen_ai.compliance.request_triggered Lists compliance-related filters that were triggered during the processing of the request, such as data privacy filters or regulatory compliance checks. keyword
gen_ai.compliance.response_triggered Lists compliance-related filters that were triggered during the processing of the response, such as data privacy filters or regulatory compliance checks. keyword
gen_ai.compliance.violation_code Code identifying the specific compliance rule that was violated. keyword
gen_ai.compliance.violation_detected Indicates if any compliance violation was detected during the interaction. boolean
gen_ai.guardrail_id Guardrail ID if a guardrail was executed. keyword
gen_ai.owasp.description Description of the OWASP risk triggered. text
gen_ai.owasp.id Identifier for the OWASP risk addressed. keyword
gen_ai.performance.request_size Size of the request payload in bytes. long
gen_ai.performance.response_size Size of the response payload in bytes. long
gen_ai.performance.response_time Time taken by the LLM to generate a response in milliseconds. long
gen_ai.performance.start_response_time Time taken by the LLM to send first response byte in milliseconds. long
gen_ai.policy.action Action taken due to a policy violation, such as blocking, alerting, or modifying the content. keyword
gen_ai.policy.confidence Confidence level in the policy match that triggered the action, quantifying how closely the identified content matched the policy criteria. keyword
gen_ai.policy.match_detail.* object
gen_ai.policy.match_detail.score float
gen_ai.policy.match_detail.threshold float
gen_ai.policy.name Name of the specific policy that was triggered. keyword
gen_ai.policy.violation Specifies if a security policy was violated. boolean
gen_ai.prompt The full text of the user's request to the gen_ai. text
gen_ai.request.id Unique identifier for the LLM request. keyword
gen_ai.request.max_tokens Maximum number of tokens the LLM generates for a request. integer
gen_ai.request.model.description Description of the LLM model. keyword
gen_ai.request.model.id Unique identifier for the LLM model. keyword
gen_ai.request.model.instructions Custom instructions for the LLM model. text
gen_ai.request.model.role Role of the LLM model in the interaction. keyword
gen_ai.request.model.type Type of LLM model. keyword
gen_ai.request.model.version Version of the LLM model used to generate the response. keyword
gen_ai.request.temperature Temperature setting for the LLM request. float
gen_ai.request.timestamp Timestamp when the request was made. date
gen_ai.request.top_k The top_k sampling setting for the LLM request. float
gen_ai.request.top_p The top_p sampling setting for the LLM request. float
gen_ai.response.error_code Error code returned in the LLM response. keyword
gen_ai.response.finish_reasons Reason the LLM response stopped. keyword
gen_ai.response.id Unique identifier for the LLM response. keyword
gen_ai.response.model Name of the LLM a response was generated from. keyword
gen_ai.response.timestamp Timestamp when the response was received. date
gen_ai.security.hallucination_consistency Consistency check between multiple responses. float
gen_ai.security.jailbreak_score Measures similarity to known jailbreak attempts. float
gen_ai.security.prompt_injection_score Measures similarity to known prompt injection attacks. float
gen_ai.security.refusal_score Measures similarity to known LLM refusal responses. float
gen_ai.security.regex_pattern_count Counts occurrences of strings matching user-defined regex patterns. integer
gen_ai.sentiment.content_categories Categories of content identified as sensitive or requiring moderation. keyword
gen_ai.sentiment.content_inappropriate Whether the content was flagged as inappropriate or sensitive. boolean
gen_ai.sentiment.score Sentiment analysis score. float
gen_ai.sentiment.toxicity_score Toxicity analysis score. float
gen_ai.system Name of the LLM foundation model vendor. keyword
gen_ai.text.complexity_score Evaluates the complexity of the text. float
gen_ai.text.readability_score Measures the readability level of the text. float
gen_ai.text.similarity_score Measures the similarity between the prompt and response. float
gen_ai.threat.action Recommended action to mitigate the detected security threat. keyword
gen_ai.threat.category Category of the detected security threat. keyword
gen_ai.threat.description Description of the detected security threat. text
gen_ai.threat.detected Whether a security threat was detected. boolean
gen_ai.threat.risk_score Numerical score indicating the potential risk associated with the response. float
gen_ai.threat.signature Signature of the detected security threat. keyword
gen_ai.threat.source Source of the detected security threat. keyword
gen_ai.threat.type Type of threat detected in the LLM interaction. keyword
gen_ai.threat.yara_matches Stores results from YARA scans including rule matches and categories. nested
gen_ai.usage.completion_tokens Number of tokens in the LLM's response. integer
gen_ai.usage.prompt_tokens Number of tokens in the user's request. integer
gen_ai.user.id Unique identifier for the user. keyword
gen_ai.user.rn Unique resource name for the user. keyword
host.containerized If the host is a container. boolean
host.os.build OS build information. keyword
host.os.codename OS codename, if any. keyword
input.type Type of Filebeat input. keyword
log.offset Log offset long

Amazon Bedrock runtime metrics include Invocations, InvocationLatency, InvocationClientErrors, InvocationServerErrors, OutputTokenCount, OutputImageCount, InvocationThrottles. These metrics can be used for various use cases including:

  • Comparing model latency
  • Measuring input and output token counts
  • Detecting the number of invocations that the system throttled
Example 
{
    "@timestamp": "2024-07-15T07:35:00.000Z",
    "agent": {
        "ephemeral_id": "63673811-d18c-4209-8818-df8b346bcb28",
        "id": "47a2173f-3f59-4a7c-a022-dee86802c2c1",
        "name": "service-integration-dev-idc-1",
        "type": "metricbeat",
        "version": "8.13.4"
    },
    "aws": {
        "cloudwatch": {
            "namespace": "AWS/Bedrock"
        }
    },
    "aws_bedrock": {
        "runtime": {
            "input_token_count": 848,
            "invocation_latency": 2757,
            "invocations": 5,
            "output_token_count": 1775
        }
    },
    "cloud": {
        "account": {
            "id": "00000000000000",
            "name": "MonitoringAccount"
        },
        "provider": "aws",
        "region": "ap-south-1"
    },
    "data_stream": {
        "dataset": "aws_bedrock.runtime",
        "namespace": "ep",
        "type": "metrics"
    },
    "ecs": {
        "version": "8.0.0"
    },
    "elastic_agent": {
        "id": "47a2173f-3f59-4a7c-a022-dee86802c2c1",
        "snapshot": false,
        "version": "8.13.4"
    },
    "event": {
        "agent_id_status": "verified",
        "dataset": "aws_bedrock.runtime",
        "duration": 174434808,
        "ingested": "2024-07-15T07:44:02Z",
        "module": "aws"
    },
    "host": {
        "architecture": "x86_64",
        "containerized": false,
        "hostname": "service-integration-dev-idc-1",
        "id": "1bfc9b2d8959f75a520a3cb94cf035c8",
        "ip": [
            "10.160.0.4",
            "172.1.0.1",
            "172.17.0.1",
            "172.19.0.1",
            "172.20.0.1",
            "172.22.0.1",
            "172.23.0.1",
            "172.26.0.1",
            "172.27.0.1",
            "172.28.0.1",
            "172.29.0.1",
            "172.30.0.1",
            "172.31.0.1",
            "192.168.0.1",
            "192.168.32.1",
            "192.168.49.1",
            "192.168.80.1",
            "192.168.224.1",
            "fe80::42:9cff:fe5b:79b4",
            "fe80::42:a5ff:fe15:d63c",
            "fe80::42:beff:fe39:f457",
            "fe80::42a:f7ff:fe6c:421d",
            "fe80::1818:53ff:fea8:3f38",
            "fe80::4001:aff:fea0:4",
            "fe80::8cfa:3aff:fedb:656a",
            "fe80::c890:29ff:fe99:ac1b",
            "fe80::fcfc:c2ff:feca:1e28"
        ],
        "mac": [
            "02-42-0D-A6-43-C0",
            "02-42-23-32-CF-25",
            "02-42-27-90-E6-54",
            "02-42-34-10-CA-62",
            "02-42-4F-1D-94-1B",
            "02-42-50-2E-CB-58",
            "02-42-5D-42-F3-1D",
            "02-42-66-9B-25-B2",
            "02-42-99-B7-1B-26",
            "02-42-9C-5B-79-B4",
            "02-42-A5-15-D6-3C",
            "02-42-A6-68-F8-E9",
            "02-42-BE-39-F4-57",
            "02-42-CE-31-B7-A3",
            "02-42-E8-F3-CF-7A",
            "02-42-F1-35-B0-41",
            "02-42-F4-2F-0F-22",
            "06-2A-F7-6C-42-1D",
            "1A-18-53-A8-3F-38",
            "42-01-0A-A0-00-04",
            "8E-FA-3A-DB-65-6A",
            "CA-90-29-99-AC-1B",
            "FE-FC-C2-CA-1E-28"
        ],
        "name": "service-integration-dev-idc-1",
        "os": {
            "codename": "bionic",
            "family": "debian",
            "kernel": "5.4.0-1106-gcp",
            "name": "Ubuntu",
            "platform": "ubuntu",
            "type": "linux",
            "version": "18.04.6 LTS (Bionic Beaver)"
        }
    },
    "metricset": {
        "name": "cloudwatch",
        "period": 300000
    },
    "service": {
        "type": "aws"
    }
}
Exported fields
Field Description Type Unit Metric Type
@timestamp Event timestamp. date
agent.id Unique identifier of this agent (if one exists). Example: For Beats this would be beat.id. keyword
aws.cloudwatch.namespace The namespace specified when query cloudwatch api. keyword
aws.metrics_names_fingerprint Autogenerated ID representing the fingerprint of the list of metrics names. keyword
aws_bedrock.runtime.bucketed_step_size keyword
aws_bedrock.runtime.image_size keyword
aws_bedrock.runtime.input_token_count The number of text input tokens. long gauge
aws_bedrock.runtime.invocation_client_errors The number of invocations that result in client-side errors. long gauge
aws_bedrock.runtime.invocation_latency The average latency of the invocations. long ms gauge
aws_bedrock.runtime.invocation_server_errors The number of invocations that result in AWS server-side errors. long gauge
aws_bedrock.runtime.invocation_throttles The number of invocations that the system throttled. long gauge
aws_bedrock.runtime.invocations The number of requests to the Converse, ConverseStream, InvokeModel, and InvokeModelWithResponseStream API operations. long gauge
aws_bedrock.runtime.legacymodel_invocations The number of requests to the legacy models. long gauge
aws_bedrock.runtime.model_id keyword
aws_bedrock.runtime.output_image_count The number of output images. long gauge
aws_bedrock.runtime.output_token_count The number of text output tokens. long gauge
aws_bedrock.runtime.quality keyword
cloud.account.id The cloud account or organization id used to identify different entities in a multi-tenant environment. Examples: AWS account id, Google Cloud ORG Id, or other unique identifier. keyword
cloud.region Region in which this host, resource, or service is located. keyword
data_stream.dataset Data stream dataset. constant_keyword
data_stream.namespace Data stream namespace. constant_keyword
data_stream.type Data stream type. constant_keyword
event.module Name of the module this data is coming from. If your monitoring agent supports the concept of modules or plugins to process events of a given source (e.g. Apache logs), event.module should contain the name of this module. constant_keyword

Amazon Bedrock guardrail metrics include Invocations, InvocationLatency, InvocationClientErrors, InvocationServerErrors, InvocationThrottles, TextUnitCount, and InvocationsIntervened. These metrics enable several use cases, such as:

  • Monitoring the latency of guardrail invocations
  • Tracking the number of text units consumed by guardrail policies
  • Detecting invocations where guardrails intervened
Example 
{
    "@timestamp": "2025-01-08T08:35:00.000Z",
    "agent": {
        "ephemeral_id": "457aa99d-9fdf-4494-9d06-31961a43f33a",
        "id": "9cdf7072-cfc1-4ad5-b68d-b51f0cd9afa8",
        "name": "elastic-agent-21656",
        "type": "metricbeat",
        "version": "8.16.2"
    },
    "aws": {
        "cloudwatch": {
            "namespace": "AWS/Bedrock/Guardrails"
        }
    },
    "aws_bedrock": {
        "guardrails": {
            "invocation_latency": 207,
            "invocations": 6,
            "invocations_intervened": 3,
            "operation": "ApplyGuardrail",
            "text_unit_count": 21
        }
    },
    "cloud": {
        "account": {
            "id": "11111111111111111",
            "name": "MonitoringAccount"
        },
        "provider": "aws",
        "region": "ap-south-1"
    },
    "data_stream": {
        "dataset": "aws_bedrock.guardrails",
        "namespace": "63909",
        "type": "metrics"
    },
    "ecs": {
        "version": "8.0.0"
    },
    "elastic_agent": {
        "id": "9cdf7072-cfc1-4ad5-b68d-b51f0cd9afa8",
        "snapshot": false,
        "version": "8.16.2"
    },
    "event": {
        "agent_id_status": "verified",
        "dataset": "aws_bedrock.guardrails",
        "duration": 100053062,
        "ingested": "2025-01-08T08:43:11Z",
        "module": "aws"
    },
    "host": {
        "architecture": "x86_64",
        "containerized": true,
        "hostname": "elastic-agent-21656",
        "ip": [
            "172.26.0.5",
            "172.29.0.2"
        ],
        "mac": [
            "02-42-AC-1A-00-05",
            "02-42-AC-1D-00-02"
        ],
        "name": "elastic-agent-21656",
        "os": {
            "family": "",
            "kernel": "5.4.0-1106-gcp",
            "name": "Wolfi",
            "platform": "wolfi",
            "type": "linux",
            "version": "20230201"
        }
    },
    "metricset": {
        "name": "cloudwatch",
        "period": 300000
    },
    "service": {
        "type": "aws"
    }
}
Exported fields
Field Description Type Unit Metric Type
@timestamp Event timestamp. date
agent.id Unique identifier of this agent (if one exists). Example: For Beats this would be beat.id. keyword
aws.cloudwatch.namespace The namespace specified when query cloudwatch api. keyword
aws.metrics_names_fingerprint Autogenerated ID representing the fingerprint of the list of metrics names. keyword
aws_bedrock.guardrails.guardrail_arn keyword
aws_bedrock.guardrails.guardrail_content_source keyword
aws_bedrock.guardrails.guardrail_policy_type keyword
aws_bedrock.guardrails.guardrail_version keyword
aws_bedrock.guardrails.invocation_client_errors The number of invocations that result in AWS client-side errors. long gauge
aws_bedrock.guardrails.invocation_latency The latency of the invocations. long ms gauge
aws_bedrock.guardrails.invocation_server_errors The number of invocations that result in AWS server-side errors. long gauge
aws_bedrock.guardrails.invocation_throttles The number of invocations that the system throttled. long gauge
aws_bedrock.guardrails.invocations The number of requests to the ApplyGuardrail API operation. long gauge
aws_bedrock.guardrails.invocations_intervened The number of invocations where the guardrails intervened. long gauge
aws_bedrock.guardrails.operation keyword
aws_bedrock.guardrails.text_unit_count The number of text units consumed by the guardrails policies. long gauge
cloud.account.id The cloud account or organization id used to identify different entities in a multi-tenant environment. Examples: AWS account id, Google Cloud ORG Id, or other unique identifier. keyword
cloud.region Region in which this host, resource, or service is located. keyword
data_stream.dataset Data stream dataset. constant_keyword
data_stream.namespace Data stream namespace. constant_keyword
data_stream.type Data stream type. constant_keyword
Changelog
Version Details Kibana version(s)
1.2.0 Enhancement (View pull request)
Add support to configure start_timestamp and ignore_older configurations for AWS S3 backed inputs.		 8.16.5 or higher
9.0.0 or higher
1.1.0 Enhancement (View pull request)
Update Kibana constraint to support 9.0.0.		 8.16.2 or higher
9.0.0 or higher
1.0.1 Enhancement (View pull request)
Add guardrail policy action details in the guardrails dashboard.		 8.16.2 or higher
1.0.0 Enhancement (View pull request)
Make Amazon Bedrock package GA.		 8.16.2 or higher
0.22.2 Enhancement (View pull request)
Add Guardrails dataset details to the AWS Bedrock integration page.		 —
0.22.1 Enhancement (View pull request)
Add minor improvements to the Overview and Guardrails dashboards.		 —
0.22.0 Enhancement (View pull request)
Add improvements to the Overview and Guardrails dashboards.		 —
0.21.0 Enhancement (View pull request)
Add dashboard for the guardrails metrics.		 —
0.20.0 Enhancement (View pull request)
Added the ability to collect and map input message/prompt content formats.		 —
0.19.0 Enhancement (View pull request)
Add new dataset guardrails for Guardrails metrics.		 —
0.18.1 Enhancement (View pull request)
Add observability category.		 —
0.18.0 Enhancement (View pull request)
Add support for Access Point ARN when collecting logs via the AWS S3 Bucket.		 —
0.17.0 Enhancement (View pull request)
Add support for function-calling output.		 —
0.16.0 Enhancement (View pull request)
Add "preserve_original_event" tag to documents with event.kind set to "pipeline_error".		 —
0.15.0 Enhancement (View pull request)
Retain contextualGroundingPolicy check details.		 —
0.14.0 Enhancement (View pull request)
Add option to check linked accounts when using log group prefixes to derive matching log groups		 —
0.13.1 Bug fix (View pull request)
Refactor get_guardrail_details and fix missing data.		 —
0.13.0 Enhancement (View pull request)
Add mapping for gen_ai.guardrail_id from output.outputBodyJson.trace.guardrail.inputAssessment.		 —
0.12.0 Enhancement (View pull request)
Support configuring the Owning Account		 —
0.11.3 Bug fix (View pull request)
Ignore non-policy data under ...trace.guardrail.		 —
0.11.2 Enhancement (View pull request)
Add reference to AWS API requests and pricing information.		 —
0.11.1 Bug fix (View pull request)
Fix whitespace issues in dashboard. Fix documentation issues.		 —
0.11.0 Enhancement (View pull request)
Improve clarity of documentation.		 —
0.10.1 Bug fix (View pull request)
Use triple-brace Mustache templating when referencing variables in ingest pipelines.		 —
0.10.0 Enhancement (View pull request)
Add aws.metrics_names_fingerprint.		 —
0.9.1 Bug fix (View pull request)
Update integration name to Amazon Bedrock in policy template.		 —
0.9.0 Enhancement (View pull request)
Allow @custom pipeline access to event.original without setting preserve_original_event.		 —
0.8.0 Bug fix (View pull request)
Update integration name to Amazon Bedrock.		 —
0.7.0 Enhancement (View pull request)
Support newer guardrails data structure.		 —
0.6.0 Enhancement (View pull request)
Add new field aws_bedrock.invocation.output.completion_text having LLM text model response. Add visualization for LLM prompt and response.		 —
0.5.0 Enhancement (View pull request)
Add processor to set cloud.account.name field for aws_bedrock runtime data stream.		 —
0.4.0 Enhancement (View pull request)
Add dot_expander processor into metrics ingest pipeline.		 —
0.3.0 Enhancement (View pull request)
Add runtime dataset for collecting runtime metrics.		 —
0.2.0 Enhancement (View pull request)
Update the kibana constraint to ^8.13.0. Modified the field definitions to remove ECS fields made redundant by the ecs@mappings component template.		 —
0.1.3 Bug fix (View pull request)
Fix name canonicalization routines.		 —
0.1.2 Bug fix (View pull request)
Add documentation image.		 —
0.1.1 Bug fix (View pull request)
Fix documentation markdown.		 —
0.1.0 Enhancement (View pull request)
Initial build.		 —