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Amazon Bedrock

Version	0.17.0 [beta] This functionality is in beta and is subject to change. The design and code is less mature than official GA features and is being provided as-is with no warranties. Beta features are not subject to the support SLA of official GA features. (View all)
Compatible Kibana version(s)	8.16.0 or higher
Supported Serverless project types What’s this?	Security Observability
Subscription level What’s this?	Basic
Level of support What’s this?	Elastic

Overview

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Amazon Bedrock is a fully managed service that makes high-performing foundation models (FMs) from leading AI startups and Amazon available for your use through a unified API. You can choose from a wide range of foundation models to find the model that is best suited for your use case. Amazon Bedrock also offers a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI. Using Amazon Bedrock, you can easily experiment with and evaluate top foundation models for your use cases, privately customize them with your data using techniques such as fine-tuning and Retrieval Augmented Generation (RAG), and build agents that execute tasks using your enterprise systems and data sources.

The Amazon Bedrock integration allows you to easily connect your Amazon Bedrock model invocation logging and runtime metrics to Elastic for seamless collection of invocation logs and runtime metrics to monitor usage.

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.

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

Compatibility

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

Data streams

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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.

Requirements

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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.
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 via the integration’s ingest pipelines.

Installing and managing an Elastic Agent

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To install and manage an Elastic Agent you have the following options:

Install a Fleet-managed Elastic Agent (recommended)

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You install Elastic Agent and use Fleet in Kibana to define, configure, and manage your agents in a central location. We recommend using Fleet management because it makes the management and upgrade of your agents considerably easier.

Install Elastic Agent in standalone mode (advanced users)

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You install Elastic Agent and manually configure the agent locally on the system where it is installed. You are responsible for managing and upgrading the agents. This approach is for advanced users only.

Install Elastic Agent in a containerized environment

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You can run Elastic Agent inside a container, either with Fleet Server or standalone. Docker images for all versions of Elastic Agent are available from the Elastic Docker registry, and we provide deployment manifests for running on Kubernetes.

To run Elastic Agent, check these requirements.

Setup

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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.

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

Logs

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Collecting Amazon Bedrock model invocation logs from S3 bucket

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When collecting logs from S3 bucket is enabled, you can retrieve logs from S3 objects that are pointed to by S3 notification events read from an SQS queue or directly polling list of S3 objects in an S3 bucket.

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

SQS notification method is enabled setting queue_url configuration value. S3 bucket list polling method is enabled setting bucket_arn configuration value and number_of_workers value. Both queue_url and bucket_arn cannot be set at the same time and at least one of the two value must be set.

Collecting Amazon Bedrock model invocation logs from CloudWatch

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When collecting logs from CloudWatch is enabled, you can retrieve logs from all log streams in a specific log group. 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.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

Metrics

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Runtime Metrics

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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

An example event for runtime looks as following:

{
    "@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

Changelog

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Changelog

Version	Details	Kibana version(s)
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.	—

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