What does Cyera actually do?

Cyera is a cloud-native data security posture management platform. It connects to cloud storage, SaaS applications, and structured and unstructured data stores, then runs discovery and classification passes that tag sensitive data such as PII, PHI, source code, and intellectual property against a taxonomy and map who and what can access it. It also flags shadow AI usage, surfacing when an employee connects an unsanctioned model to a sanctioned data source. In July 2026 it agreed to acquire Oasis Security for roughly $1 billion, adding inventory of non-human identities such as service accounts, API keys, and AI agent credentials, along with what each one is entitled to reach. The architecture underneath all of this runs on scheduled discovery and classification passes. It is a strong answer to "where is our sensitive data and who can reach it," and a separate question from "should this specific request reach the model right now."

How is DeepInspect different from Cyera?

Cyera and DeepInspect answer different questions at different points in the AI request. Cyera discovers and classifies sensitive data across cloud stores, SaaS applications, and data platforms, and its Oasis Security acquisition adds inventory of non-human identities and what they can reach, all evaluated on a scan cadence. DeepInspect is a stateless proxy directly in the path between an authenticated user or agent and the LLM it is calling. It evaluates identity, data classification, and policy for that specific request before the request reaches the model, permits, redacts, or blocks it accordingly, and commits a signed audit record of the decision. Cyera's output describes the environment as of the last scan. DeepInspect's output is a decision on one request, made in the time it takes that request to pass through the proxy, with the record written independently of the application that sent it. Most regulated deployments end up needing both jobs done, by different tools.

Does the Oasis Security acquisition make Cyera an AI enforcement platform?

The Oasis Security acquisition adds identity governance to Cyera's platform, and identity governance is a different job from enforcement. Oasis brought discovery and inventory of service accounts, OAuth tokens, secrets, and, increasingly, AI agent identities, along with the entitlements attached to each one. That is real, useful coverage of a problem most enterprises have mostly ignored. Identity governance answers which agents exist and what they are allowed to reach, assessed on a discovery schedule. Enforcement evaluates a specific request at the moment it is made and decides whether to let it through before it reaches the model. A company can own excellent inventory of every AI agent's credentials and still have nothing sitting in the request path itself. The acquisition is a strong signal that identity-bound governance of AI-agent activity has become a validated buying category, which matters for anyone building a shortlist. Governing what an agent may reach and deciding what one call actually does remain two separate architectural jobs.

Can Cyera and an inline enforcement layer run together?

Yes, and for most regulated deployments that combination is the realistic end state rather than a compromise. Cyera's discovery and classification work identifies where sensitive data sits and, since the Oasis deal, which non-human identities can reach it. That inventory is valuable input to an enforcement layer's policy: knowing that a specific service account handles PHI makes it possible to write a tighter rule for the requests it sends to a model. An inline AI policy gateway then handles the part a posture tool does not: evaluating each request against that policy at the moment it happens, before it reaches the model, and producing an independent record of the decision. A platform team standardizing on Cyera for data and identity inventory, with an enforcement layer in front of its model traffic, ends up with fuller coverage than either tool provides alone. The two categories were built for adjacent jobs, not competing ones.

What should a CISO ask in a Cyera evaluation?

Start with what job the evaluation is actually solving. If the open question is where sensitive data lives across cloud storage and SaaS applications, Cyera's core DSPM function is built for exactly that, and the evaluation should focus on coverage of the specific cloud and SaaS estate in question. If the open question includes AI agent activity, ask whether the Oasis-derived non-human identity inventory covers the specific identity providers, secret stores, and agent frameworks already in production, since coverage gaps are common right after an acquisition closes. Separately, ask who or what evaluates a specific model call at the moment it happens, and where that evaluation's audit record is written and by whom. If the answer to that last question is that the application logs it, the evaluation has found the gap that a posture and inventory platform, however strong, does not close on its own. Name that gap to the committee before the contract is signed, not after the first regulator asks for evidence.

Is BigID a reasonable alternative to Cyera?

BigID is one of the more established names in the same DSPM category as Cyera, with a longer history covering on-premises and hybrid data estates alongside cloud. Both platforms run discovery and classification passes against structured and unstructured data, apply a sensitivity taxonomy, and report on access and posture. The evaluation questions are the same ones that apply across the category: which data sources and cloud providers does the platform actually cover in your environment, how current is the classification given your scan cadence, and does the platform's non-human identity coverage, if any, match the identity providers and secret stores you run today. Neither BigID nor Cyera was built as a request-time enforcement layer for AI traffic, so a team evaluating either one for that specific job will find the same architectural gap in both. Choosing between them is a question of data-source coverage and existing vendor relationships, not a substitute for deciding how live model calls get evaluated.

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August 22, 2026

Cyera Alternatives: Data Security Platforms vs. Inline AI Enforcement

Cyera is a cloud-native data security posture platform that discovers and classifies sensitive data across cloud stores, SaaS apps, and, since its Oasis Security acquisition, non-human identities. This piece walks through seven alternative categories, including data classification tools, non-human identity governance, and inline AI policy gateways, with an honest fit test for each one.

ByParminder Singh· Founder & CEO, DeepInspect Inc.

Comparisons & Alternativesdata-loss-preventiondlpai-governanceai-securityshadow-aiidentity-and-authorization

Cyera Alternatives: Data Security Platforms vs. Inline AI Enforcement

Cyera scans cloud storage, SaaS applications, and structured data stores, then tags what it finds against a sensitivity taxonomy: PII, PHI, source code, intellectual property. On July 28, it agreed to pay roughly $1 billion for Oasis Security, adding inventory of service accounts, API keys, and AI agent identities to that picture. I wrote about what that deal signals for the AI-agent identity market the week it closed. A team evaluating Cyera today is really evaluating two jobs at once: finding and classifying data, and knowing what identities, human or otherwise, can reach it. Neither job is the same as deciding, in the moment before a prompt reaches an LLM, whether that specific request should go through.

TL;DR

Cyera discovers and classifies sensitive data across cloud stores, SaaS apps, and, since its Oasis Security deal, non-human identities, on a scan cadence.
Its architecture is built for posture and inventory, not for stopping one specific LLM call before it happens.
Seven categories cover this market: DSPM, dedicated classification tools, non-human identity governance, data-aware CSPM, LLM observability, next-gen DLP with AI add-ons, and inline AI policy gateways.
Pick the category that matches the actual gap: finding data, inventorying identities, watching model behavior, or enforcing policy on live traffic.
DeepInspect covers the last one: identity-bound, per-request enforcement with a signed audit record at the AI request boundary.

What Cyera does

Cyera is a cloud-native data security posture management (DSPM) platform. It connects to cloud storage buckets, SaaS applications, and structured and unstructured data stores, then runs discovery and classification passes that tag sensitive data against a taxonomy and track who and what can access it. That scope has expanded twice in the past year: first into shadow AI discovery, flagging when an employee connects an unsanctioned model to a sanctioned data source, and then into non-human identity through the Oasis Security acquisition, which adds inventory of service accounts, OAuth tokens, and AI agent identities, plus what each one is entitled to reach.

That is a genuine expansion into AI-agent territory, and it earns a direct answer: identity-bound governance of AI-agent activity is now on Cyera's roadmap in a way it was not twelve months ago. The architecture underneath that expansion still runs on a discovery and classification cadence. A scan that finishes at 9:14 a.m. describes the environment as it stood at 9:14 a.m., not the prompt an agent sent to a model one minute later.

Google Mandiant's M-Trends 2026 report, built on more than 500,000 hours of incident response, found the median handoff from initial access to a secondary threat group fell to 22 seconds by 2025. Against that tempo, a posture and inventory view is a forensic record: what existed and what was entitled to what, at the moment the scan ran. Cyera built a serious discovery and classification product. The distinction worth naming plainly is architectural, not a critique of the engineering. A scan describes a state. Enforcement decides what happens to one request, right now.

Alternatives to evaluate

The category question matters more than any single vendor name. These are the seven types of tools that come up in a Cyera evaluation, what each is actually built to do, and who should reach for it.

Cloud DSPM platforms. Cyera's own category: cloud-native discovery and classification across storage, SaaS, and structured data stores, with sensitivity tagging and access mapping. BigID is the other name that comes up most often here, running a similar model with a longer history across on-premises and hybrid data estates. Best for a security team whose first, unanswered question is where sensitive data actually lives across a sprawling cloud footprint.
Dedicated data classification and discovery tools. Narrower than full DSPM: these connect to specific repositories, file shares, and databases and apply classification rules without the full posture-management console. Best for a team that already runs a posture tool and needs a second, more granular pass on one data source, such as a file share nobody has audited in years.
Non-human identity (NHI) governance platforms. The category Cyera bought its way into with Oasis: inventory of service accounts, API keys, OAuth tokens, and AI agent identities, plus the entitlements attached to each one. NIST's AI agent identity and authorization framework calls this identity-inventory half of the problem Pillar 1, a prerequisite for everything downstream. Best for a platform team that cannot answer how many AI agents hold live credentials without opening three consoles and asking around.
Data-aware cloud security posture management (CSPM). Broader infrastructure and misconfiguration tools that have added data-sensitivity context to existing cloud posture scans. Best for a team that started the evaluation from cloud misconfiguration risk and wants one console rather than a second standalone tool.
LLM observability and evaluation platforms. These capture traces, latency, token spend, and model-quality evaluations for debugging and performance work, built for engineers improving a model pipeline rather than for a security decision about one request. Best for an AI platform team optimizing accuracy and cost, running alongside a security control rather than in place of one.
DLP and CASB platforms with AI add-ons. Traditional data-loss-prevention and cloud-access-security-broker vendors have started shipping modules that flag AI-tool usage. IBM's Cost of Data Breach Report found that one in five of the 600 breached organizations it studied had a breach linked to shadow AI, the gap these add-ons chase. The add-ons run at the network layer, underneath the TLS encryption that protects the API call to the model, so they see that a connection to an AI provider domain happened without seeing what was inside the prompt. Best for a team extending an existing DLP investment, paired with a tool that can see inside the request itself.
Inline AI policy gateways. The category DeepInspect sits in: a layer that intercepts the HTTP call between an authenticated user or agent and the LLM, evaluates identity and data classification against policy, and permits, redacts, or blocks the request before it reaches the model. Best for a team whose gap sits at the moment of the call, where an authenticated, entitled user could still send something they should not send, and where a regulator or incident responder will eventually ask for the record of what happened and why.

Questions worth asking in the room

Three questions separate a posture tool from an enforcement layer in an actual technical review, and I ask them in this order. Does the product see the request before it reaches the model, or does it see a record of the request afterward? Does the identity attached to a decision come from your own identity provider, or from a service account the product infers after the fact? The third question is about a policy change: whether every future request gets evaluated against the new version immediately, or the update waits for the next scheduled scan.

My honest opinion: half the RFPs I read for "AI data security" bundle four separate jobs into one line item, discovery, classification, identity inventory, and enforcement, and that is exactly how a security team ends up with an excellent data catalog and no enforcement point. I watched a vendor review once where the committee spent twenty minutes debating whether a DSPM dashboard's amber risk score counted as monitoring AI traffic. It didn't, and nobody in the room wanted to say so first.

DeepInspect

DeepInspect sits at a different point in the same evaluation than Cyera does. It is a stateless proxy between authenticated users and agents and any HTTP-based LLM endpoint. It evaluates identity, data classification, and policy on every request, in the path, before that request reaches the model, and it produces a signed, tamper-evident record of the decision.

Cyera and the rest of the DSPM category answer what data exists and what identities can reach it. DeepInspect answers a narrower, later question: given this identity, this policy, and this specific prompt, does the request go through right now. The two jobs compose well. A platform team that has used Cyera, or a comparable tool, to inventory its data and its non-human identities still needs something evaluating the live call, and a team running DeepInspect still benefits from knowing where its sensitive data lives before that call is ever made.

I wrote a fuller side by side on how the two products differ in DeepInspect vs Cyera. If you are facing the August deadline, let's talk.

Frequently asked questions

What does Cyera actually do?: Cyera is a cloud-native data security posture management platform. It connects to cloud storage, SaaS applications, and structured and unstructured data stores, then runs discovery and classification passes that tag sensitive data such as PII, PHI, source code, and intellectual property against a taxonomy and map who and what can access it. It also flags shadow AI usage, surfacing when an employee connects an unsanctioned model to a sanctioned data source. In July 2026 it agreed to acquire Oasis Security for roughly $1 billion, adding inventory of non-human identities such as service accounts, API keys, and AI agent credentials, along with what each one is entitled to reach. The architecture underneath all of this runs on scheduled discovery and classification passes. It is a strong answer to "where is our sensitive data and who can reach it," and a separate question from "should this specific request reach the model right now."
How is DeepInspect different from Cyera?: Cyera and DeepInspect answer different questions at different points in the AI request. Cyera discovers and classifies sensitive data across cloud stores, SaaS applications, and data platforms, and its Oasis Security acquisition adds inventory of non-human identities and what they can reach, all evaluated on a scan cadence. DeepInspect is a stateless proxy directly in the path between an authenticated user or agent and the LLM it is calling. It evaluates identity, data classification, and policy for that specific request before the request reaches the model, permits, redacts, or blocks it accordingly, and commits a signed audit record of the decision. Cyera's output describes the environment as of the last scan. DeepInspect's output is a decision on one request, made in the time it takes that request to pass through the proxy, with the record written independently of the application that sent it. Most regulated deployments end up needing both jobs done, by different tools.
Does the Oasis Security acquisition make Cyera an AI enforcement platform?: The Oasis Security acquisition adds identity governance to Cyera's platform, and identity governance is a different job from enforcement. Oasis brought discovery and inventory of service accounts, OAuth tokens, secrets, and, increasingly, AI agent identities, along with the entitlements attached to each one. That is real, useful coverage of a problem most enterprises have mostly ignored. Identity governance answers which agents exist and what they are allowed to reach, assessed on a discovery schedule. Enforcement evaluates a specific request at the moment it is made and decides whether to let it through before it reaches the model. A company can own excellent inventory of every AI agent's credentials and still have nothing sitting in the request path itself. The acquisition is a strong signal that identity-bound governance of AI-agent activity has become a validated buying category, which matters for anyone building a shortlist. Governing what an agent may reach and deciding what one call actually does remain two separate architectural jobs.
Can Cyera and an inline enforcement layer run together?: Yes, and for most regulated deployments that combination is the realistic end state rather than a compromise. Cyera's discovery and classification work identifies where sensitive data sits and, since the Oasis deal, which non-human identities can reach it. That inventory is valuable input to an enforcement layer's policy: knowing that a specific service account handles PHI makes it possible to write a tighter rule for the requests it sends to a model. An inline AI policy gateway then handles the part a posture tool does not: evaluating each request against that policy at the moment it happens, before it reaches the model, and producing an independent record of the decision. A platform team standardizing on Cyera for data and identity inventory, with an enforcement layer in front of its model traffic, ends up with fuller coverage than either tool provides alone. The two categories were built for adjacent jobs, not competing ones.
What should a CISO ask in a Cyera evaluation?: Start with what job the evaluation is actually solving. If the open question is where sensitive data lives across cloud storage and SaaS applications, Cyera's core DSPM function is built for exactly that, and the evaluation should focus on coverage of the specific cloud and SaaS estate in question. If the open question includes AI agent activity, ask whether the Oasis-derived non-human identity inventory covers the specific identity providers, secret stores, and agent frameworks already in production, since coverage gaps are common right after an acquisition closes. Separately, ask who or what evaluates a specific model call at the moment it happens, and where that evaluation's audit record is written and by whom. If the answer to that last question is that the application logs it, the evaluation has found the gap that a posture and inventory platform, however strong, does not close on its own. Name that gap to the committee before the contract is signed, not after the first regulator asks for evidence.
Is BigID a reasonable alternative to Cyera?: BigID is one of the more established names in the same DSPM category as Cyera, with a longer history covering on-premises and hybrid data estates alongside cloud. Both platforms run discovery and classification passes against structured and unstructured data, apply a sensitivity taxonomy, and report on access and posture. The evaluation questions are the same ones that apply across the category: which data sources and cloud providers does the platform actually cover in your environment, how current is the classification given your scan cadence, and does the platform's non-human identity coverage, if any, match the identity providers and secret stores you run today. Neither BigID nor Cyera was built as a request-time enforcement layer for AI traffic, so a team evaluating either one for that specific job will find the same architectural gap in both. Choosing between them is a question of data-source coverage and existing vendor relationships, not a substitute for deciding how live model calls get evaluated.

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