BEDROCK DATA VS CYERA
Do not buy the demo. Test it on your data.
Bedrock Data and Cyera both promise fast, accurate data security. The real difference is how they discover, classify, and protect sensitive data at scale, and how much work they leave behind for your team. Test both platforms on your hardest data source.
Where Bedrock Data pulls ahead
Your data stays in your environment.
Bedrock Data discovers, classifies, and analyzes data where it lives. Only documented metadata is sent to the Metadata Lake.
Recognize every copy and derivative.
Bedrock Data fingerprinting connects sensitive copies, fragments, and transformed content back to their source, even when the filename, format, or location changes.
Secure data and AI from one context.
The Metadata Lake connects sensitivity, access, activity, lineage, business context, and supported AI systems, then makes that context available across your stack.
Scale up for the scan. Back to zero when it is done.
Bedrock Data Serverless Outposts expand for the work and scale back to zero when it is complete, leaving no permanent scanning fleet to manage.
Prove scale on your data.
Test the same source, scope, and refresh cadence. Then compare time to results, cloud cost, coverage, and the work required from your team.
How Bedrock Data compares
Cyera | Bedrock Data | |
|---|---|---|
Where data is processed | Cyera offers full-SaaS and Outpost deployment options. Confirm where discovery and classification happen for each source and what information leaves your environment. | Bedrock Data discovers, classifies, and analyzes data in your environment. Only documented metadata is sent to the Metadata Lake. |
Operational overhead | Cyera describes an agentless SaaS experience, with connector and Outpost options for certain environments. Validate the customer-side resources and administration required by the chosen deployment. | Bedrock Data Serverless Outposts scale for each scan, rebalance automatically, and scale back to zero when the work is complete. |
Scale proof | Cyera publishes strong scale and precision claims, including smart sampling and dynamic scaling. Test those claims on the sources, formats, and refresh cadence that matter to you. | Adaptive Scanning reduces redundant processing, skips unchanged data, and can be evaluated with agreed success criteria and your own cloud cost allocation. |
Classification | Cyera emphasizes AI-native, context-enriched classification and reports up to 95% precision across supported data. | Bedrock Data trains classifiers on your taxonomy and custom data types, then measures results against a precision protocol agreed before the evaluation. |
Lineage | Cyera Data Lineage links related file versions across supported systems using access activity and file similarity. Confirm source coverage and beta or general-availability status for your use case. | Correlation-Based Lineage and content-aware fingerprinting connect copies, fragments, and transformed data across supported environments and policy boundaries. |
AI security | Cyera offers AI posture, agent visibility, and runtime protection through its expanding AI security portfolio. | Bedrock Data uses the same Metadata Lake to connect supported agents, knowledge sources, identities, reachable data, runtime activity, and an AI Data Bill of Materials. |
Context beyond DSPM | Cyera combines DSPM with access monitoring, DLP, remediation, and AI security in its platform. | The Metadata Lake exposes shared data context through APIs and integrations for security, governance, access, AI, incident response, and ticketing workflows. |
Where data is processed
Cyera
Cyera offers full-SaaS and Outpost deployment options. Confirm where discovery and classification happen for each source and what information leaves your environment.
Bedrock Data
Bedrock Data discovers, classifies, and analyzes data in your environment. Only documented metadata is sent to the Metadata Lake.
Operational overhead
Cyera
Cyera describes an agentless SaaS experience, with connector and Outpost options for certain environments. Validate the customer-side resources and administration required by the chosen deployment.
Bedrock Data
Bedrock Data Serverless Outposts scale for each scan, rebalance automatically, and scale back to zero when the work is complete.
Scale proof
Cyera
Cyera publishes strong scale and precision claims, including smart sampling and dynamic scaling. Test those claims on the sources, formats, and refresh cadence that matter to you.
Bedrock Data
Adaptive Scanning reduces redundant processing, skips unchanged data, and can be evaluated with agreed success criteria and your own cloud cost allocation.
Classification
Cyera
Cyera emphasizes AI-native, context-enriched classification and reports up to 95% precision across supported data.
Bedrock Data
Bedrock Data trains classifiers on your taxonomy and custom data types, then measures results against a precision protocol agreed before the evaluation.
Lineage
Cyera
Cyera Data Lineage links related file versions across supported systems using access activity and file similarity. Confirm source coverage and beta or general-availability status for your use case.
Bedrock Data
Correlation-Based Lineage and content-aware fingerprinting connect copies, fragments, and transformed data across supported environments and policy boundaries.
AI security
Cyera
Cyera offers AI posture, agent visibility, and runtime protection through its expanding AI security portfolio.
Bedrock Data
Bedrock Data uses the same Metadata Lake to connect supported agents, knowledge sources, identities, reachable data, runtime activity, and an AI Data Bill of Materials.
Context beyond DSPM
Cyera
Cyera combines DSPM with access monitoring, DLP, remediation, and AI security in its platform.
Bedrock Data
The Metadata Lake exposes shared data context through APIs and integrations for security, governance, access, AI, incident response, and ticketing workflows.
Comparison reflects public Cyera materials, the supplied competitive deck, and Bedrock Data product documentation as of September 2026. Product scope and release status change quickly. Validate coverage, performance, data handling, and commercial terms in your own environment.
What a feature checklist will not tell you
“Agentless” does not tell you where your data is processed
It describes how the platform connects. It does not explain where discovery and classification happen, what information leaves your environment, or what your team must continue to operate.
A speed claim is not your result
Data volume, file types, warehouse design, refresh cadence, and scan depth all affect performance. Test the data source that matters most to your security program.
“Unified” should survive an end-to-end test
Start with a sensitive asset. Trace who and what can reach it, how it moved, which policy fired, who owns the fix, and whether the finding closes when the exposure is removed.
AI security changes too quickly for checklist buying
Agent inventories and runtime controls evolve every quarter. Test the supported AI systems you use now and confirm that the same data and identity context follows them from posture to enforcement.
Built differently.
Proven where others break.
Your data stays in your environment.
Bedrock Data performs discovery, classification, and analysis where the data lives. Only documented metadata is sent to the Metadata Lake.
No scanners sitting idle.
Compute expands for the scan and scales back to zero afterwards, so there is no permanent scanning fleet to maintain.
Recognize what spread.
Content-aware fingerprinting links copies, fragments, and derivatives back to the source across supported environments.
Make context reusable.
One continuously updated context layer connects data security, governance, access, incident response, and AI workflows.
One estate. One honest test.
Start with the source most likely to expose architectural limits: your largest warehouse, messiest file estate, strictest data boundary, or most important AI knowledge base. Give both platforms the same scope and success criteria. Measure deployment effort, data flow, infrastructure footprint, coverage, classification quality, effective access, lineage, refresh time, and total operating cost.
