Bedrock Data vs. Cyera: Metadata Lake vs. Cloud DSPM

Key takeaways

• Findings or context. Cyera's output is centered on findings: what is sensitive, where, and how severe. Bedrock Data's output is a context model, one graph of sensitivity, identity, and exposure, built for the rest of your stack to query.
• Full-estate coverage, not a representative slice. Serverless scale-out keeps unit scan cost sublinear as volume grows, so coverage doesn't have to shrink as the estate does.
• Entitlement depth is where the two separate. Bedrock Data resolves nested and chained roles as a graph rather than a flat permissions list. That matters when who can reach the data counts as much as where it lives.
• The identity numbers make the case. In Bedrock Data's own telemetry, the median service account or OAuth app can reach 55 datastores against three to four for the median employee, and 79% can reach stored secrets.
• Both platforms cover AI now. Cyera shipped inline prompt blocking and agent file lineage in 2026. The remaining difference is what sits underneath the coverage, and a parallel POC is what settles it.

Most teams arrive at this comparison mid-event: an AI rollout security has to sign off on, a cloud migration, an audit that wants proof rather than a policy. The question underneath all three is the same: what sensitive data do we have, and who or what can actually reach it?

Cyera is a cloud-native, agentless DSPM built for fast, broad discovery across cloud and SaaS, with connector-based reach into hybrid and on-premises environments. Bedrock Data is an AI and data security platform that serves as the enterprise's Data Context System of Record: one authoritative model of what data you have, how sensitive it is, who and what can actually reach it, and where it flows, distributed to every tool in your stack. It is agentless and analyzes data in place, so raw data is never copied or moved.

The distinction that matters is not speed or cloud footprint. It is what each platform produces. A scanner produces findings, and findings live in the console that generated them. A context model produces something other systems can query. That is a different job, and it is why the two feel similar in a demo and diverge in production.

For the quick verdict, see our Bedrock vs. Cyera hub.

It is worth naming the gap both categories sit inside. No existing layer (gateways, CNAPP, AI-SPM inventories) understands the sensitivity or policy status of the data involved. Every platform in this market hands you one more console to check. The question worth asking is which one gives the consoles you already own the data context they are missing. Not a single pane of glass: a single source of truth.

Bedrock Data vs. Cyera at a glance

The table below mirrors the quick-reference version on the parent hub. The sections that follow go one layer deeper into why each row is true.

Company snapshots

What is Bedrock Data?

Bedrock Data is a data context platform built on the Metadata Lake, a single graph that combines sensitivity, duplicate and similarity detection, entitlements, and access rather than treating them as separate outputs. Its serverless scale-out architecture covers the full estate at petabyte scale, so coverage is not cut to control cost, and it exposes the resulting model through an API-first interface rather than only a console.

What is Cyera?

Cyera is a cloud-native DSPM platform known for fast, agentless discovery and AI-driven classification. It covers multi-cloud and SaaS environments natively, with hybrid and on-premises reach extended through connectors.

What each platform produces

Cyera's agentless model favors speed: connect many cloud and SaaS sources at once and get discovery results quickly. The output is a set of findings: this store holds this kind of sensitive data, at this severity, rendered in Cyera's console and exportable from it.

Bedrock Data's output differs in kind. The Metadata Lake holds sensitivity, duplicate and similarity detection, entitlement, and identity as one continuously updated model, and exposes it through an API-first interface so the rest of the stack can query it. Your DLP, IAM, and SIEM stop operating on their own partial view of the data and start operating on the same one.

This is the difference that shows up six months in. A findings list ages the moment it is generated and has to be re-run. A context model is a dependency other tools build on. Teams whose estate is small enough for either platform to scan fully and quickly may not feel it early; it compounds with data volume, and with the number of tools that need to agree about the same data.

Coverage and cost at scale

Scan architecture becomes a line item as volume grows, and it is where cost-constrained platforms quietly reduce coverage by skipping data stores or sampling which stores to look at, to keep the bill flat. Bedrock Data's serverless scale-out is designed so unit cost stays sublinear as volume rises, which is what makes full-estate coverage affordable rather than aspirational: the whole risk surface is analyzed in place, not a representative slice of it.

Pricing depends on each vendor's current commercial model, so request a live cost projection from both vendors against your real data volume rather than a list price. This is where architectures that look similar on paper diverge most.

False positives and classification accuracy

Cyera's AI-driven classification is a strength in content-pattern matching. It is good at identifying what looks sensitive based on the data itself. Bedrock Data adds entitlement and exposure context on top of the sensitivity signal. That combination can reduce false positives that are technically sensitive but not realistically at risk, such as a sensitive file in an archive with no live access path to it. These are complementary lenses more than a strict better-or-worse comparison: content classification tells you what is sensitive; entitlement and exposure context tells you whether it is reachable.

That distinction is not cosmetic. Every decision downstream (a DLP rule, an access review, an approval for a new AI workload) is only as good as the data understanding beneath it.

Entitlement and access depth

Cyera surfaces identity and access context as part of its findings. Bedrock Data's Metadata Lake goes further, resolving nested and chained roles (group inheritance, service-account paths) through to their effective outcome rather than presenting them as a flat permissions list. Layered permission models, like nested role hierarchies in Snowflake or AWS role chains, are hard to reason about as a flat list and straightforward as a graph.

The reason it matters is in the numbers. Across the environments in Bedrock Data's exposure study (41,000+ datastores, about 21 PB analyzed), the median OAuth app or service account can reach 55 datastores, against three to four for the median employee. 79% of those apps can reach stored secrets such as API keys and tokens. Scoring a datastore's sensitivity without resolving how many identities can open it is scoring the wrong object.

AI and agent coverage

The more consequential axis now is what happens as sensitive data moves into RAG pipelines and model training, and both platforms have moved here. Any comparison written before spring 2026 is out of date.

Cyera shipped Browser Shield in March 2026, which blocks sensitive prompts inline before they reach external AI tools, along with data lineage that maps how agents move and transform files. In July 2026, it announced an agreement to acquire Oasis Security to deepen non-human identity coverage. Treat "does it cover AI" as answered by both.

The remaining difference is what sits underneath. Bedrock Data resolves AI agents as identities inside the same entitlement graph as human users, joined to the classification of the data they can reach, so "what can this agent actually get to" resolves against the data estate rather than against a list of the agents themselves. Bedrock Data is not the inventory system of record for your agents; the value is the link between whatever inventory you have and the sensitivity of the data underneath it.

Which should you choose?

Choose Cyera if fast initial discovery across a broad cloud and SaaS footprint is the immediate priority, your estate is early in its scaling curve, and a findings console is the output your team needs.

Choose Bedrock Data if entitlement resolution matters as much as discovery, if you are operating at the scale where coverage tradeoffs start to bite, or if you need the same data context available to the other tools in your stack rather than locked inside one platform.

Either way, the table above is a starting point, not a decision. A side-by-side proof of concept on your actual environment will tell you more than any comparison page, including this one.

The right choice depends on findings or context

The right choice depends on whether you need findings or context. Fast, broad discovery with results in a console favors Cyera. Depth of entitlement resolution, full-estate coverage at scale, and context your existing tools can consume favor Bedrock Data. Either way, a parallel proof of concept will tell you more than any comparison page, including this one.

So run both against the same estate, in the same window, and ask each to answer the same question: for one sensitive datastore, show every identity that can reach it, and how. Get a demo or start on Bedrock Data Free for Snowflake and Cortex.

FAQs

Is Bedrock Data a good Cyera alternative for multi-cloud environments?

Teams evaluate Bedrock Data as a Cyera alternative when entitlement depth and context distribution matter as much as discovery speed, particularly at petabyte scale, or where other tools in the stack need the same data context. Cyera remains the stronger fit where fast, broad initial discovery is the priority. A side-by-side proof of concept settles it faster than either vendor's material.

Which is cheaper, Bedrock Data or Cyera?

It depends on data volume and re-scan frequency. Request a live cost projection from both vendors against your own environment rather than relying on list pricing, since scan economics diverge most at high volume.

What are the advantages of a Metadata Lake for cloud DSPM?

A Metadata Lake unifies sensitive data, access permissions, lineage, and identity relationships into a single, continuously updated graph. Two things follow. Risk gets prioritized on effective exposure rather than sensitivity alone: a sensitive file that nothing can reach ranks below a moderately sensitive one that 200 identities can. And because the model is queryable rather than confined to a console, your DLP, IAM, and SIEM can act on the same context instead of each maintaining a partial copy. That gives security teams richer context for prioritizing risk than data discovery findings alone can provide.

Which platform is better for large-scale cloud DSPM deployments?

Both support enterprise cloud environments. At petabyte scale, the deciding factors are usually whether coverage holds without stores being skipped to control cost, and how deep entitlement resolution goes, which is where Bedrock Data is typically the stronger fit. Below that scale, the gap narrows, and discovery speed matters more.

What should you evaluate when comparing Bedrock Data vs. Cyera for cloud DSPM?

Look past discovery speed and compare how each platform handles continuous scanning, entitlement visibility, data residency, scalability, and long-term operational cost. One concrete test cuts through most of it: pick a sensitive datastore and ask each platform to list every identity that can reach it, including service accounts and AI agents, and the path by which each one gets there.

What can a metadata lake do that a cloud DSPM cannot?

A cloud DSPM answers questions inside its own product: where is sensitive data, and how bad is this finding? A metadata lake answers those and then makes the answer available to everything else: the same sensitivity, identity, and lineage model queried by your DLP, IAM, and SIEM. The practical difference is whether your other security tools keep operating on their own partial view of the data, or share one.