Cyera alternatives expose the real gap in AI data security

At a glance

What this is: This buyer's guide compares Cyera alternatives and finds that the central issue is not cloud discovery alone, but whether a platform can also remediate data movement across SaaS, endpoint, browser, and AI agent surfaces.

Why it matters: For IAM practitioners, the article matters because AI agents and MCP workflows behave like high-risk data movers that require governance over access, attribution, and remediation, not just posture visibility.

By the numbers:

• 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems (39%), inappropriately sharing sensitive data (31%), and revealing access credentials (23%).
• The average estimated time to remediate a leaked secret is 27 days, despite 75% of organisations expressing strong confidence in their secrets management capabilities.

👉 Read Strac's Cyera alternative guide for AI data security and MCP coverage

Context

AI data security is no longer just a cloud discovery problem. When sensitive data moves through SaaS apps, browser sessions, endpoint copy-paste, and AI agent tool calls, a posture-only view leaves the control gap untouched. The primary issue in this article is whether security teams can govern data flow at runtime, not just classify it after the fact.

That gap matters because AI agents and MCP integrations behave like new access pathways into business data. In practice, teams comparing Cyera alternatives are really deciding whether they need DSPM alone or a broader data security model that can detect, attribute, and stop sensitive data from reaching model context windows and downstream applications.

Key questions

Q: What breaks when AI agents can retrieve business data without runtime auditability?

A: When AI agents can retrieve business data without runtime auditability, security teams lose the ability to prove what data entered the model context, who authorised the request, and whether the output exceeded policy. That breaks incident reconstruction, compliance evidence, and access review because the access event is invisible at the moment it matters. Runtime logs must tie each retrieval to source, purpose, and downstream use.

Q: Why do AI systems complicate traditional data security controls?

A: AI systems can consume, transform, and recombine sensitive data in ways that traditional static controls do not model well. The risk is not only unauthorized access, but also unintended output that creates new sensitive content. That is why data classification, purpose limitation, and runtime enforcement have to work together.

Q: What do security teams get wrong about DSPM for AI workflows?

A: Security teams often assume DSPM coverage means the data problem is solved. In reality, DSPM answers where data is and how risky it looks, but it does not by itself stop a Slack message, redact a Salesforce comment, or block an MCP retrieval. The mistake is treating inventory as control.

Q: How should organisations decide between discovery and active remediation?

A: Organisations should choose active remediation when data moves frequently through SaaS, browser, endpoint, or AI agent workflows and exposure time matters. Discovery is enough for some posture programmes, but if the business depends on collaboration tools and agentic AI, enforcement has to happen inline before the data reaches model context or external recipients.

Technical breakdown

Why cloud DSPM misses AI agent and MCP data movement

Cloud DSPM is designed to find and classify sensitive data at rest across object stores and warehouses. It is strong at answering where data lives, but it does not automatically inspect runtime transfers between SaaS tools, browser prompts, endpoint copy-paste, or AI agent tool calls. MCP, the Model Context Protocol, introduces a new path where an AI agent can retrieve SaaS data and feed it into model context without a cloud storage event ever occurring. That changes the security boundary from storage to transaction.

Practical implication: teams should map which controls see data at rest versus which controls inspect active AI and SaaS data flows.

How remediation changes the control model for SaaS DLP

Discovery tells you a record exists. Remediation changes what happens next. In SaaS DLP, that means the control can redact, mask, tombstone, quarantine, or revoke access in the workflow where the leak occurs, rather than opening another ticket for a human to interpret later. This matters because modern data leakage often happens in Slack, Gmail, Salesforce, and collaborative workspaces, where a delayed response leaves sensitive data exposed for days. A control plane that cannot act inline is fundamentally a different class of capability.

Practical implication: evaluate whether the platform can enforce policy inline in SaaS, not just export findings to another queue.

Why AI agent governance needs per-tool-call auditability

AI agents and MCP tools create a traceability problem as much as a protection problem. If a model can retrieve data from multiple systems, security teams need to know which tool was called, what data was returned, who authorised the workflow, and whether the output exceeded intended scope. Without per-tool-call auditability, compliance, incident response, and access review all lose evidentiary value. This is where identity and data governance intersect: the agent acts like a runtime non-human identity, but one whose access path is mediated through data tools and model context rather than a standard login.

Practical implication: require logging that ties each AI agent retrieval to the source system, policy decision, and downstream output.

NHI Mgmt Group analysis

AI agents are becoming a governed access layer, not just a productivity layer. The article's core tension is that AI agents now move data across multiple systems, which means they behave like non-human identities with delegated access. When policy is limited to cloud classification, governance fails at the point of use. Practitioners should treat agentic workflows as part of the identity and access control surface, not as a separate AI convenience feature.

MCP creates a control-plane problem for data security. The Model Context Protocol is useful precisely because it lets agents query live business systems, but that also means the security boundary shifts from file storage to runtime retrieval. If security teams cannot see tool calls, they cannot prove what data entered the context window or whether the output crossed policy lines. The named concept here is MCP visibility gap: a blind spot where retrieval, attribution, and enforcement are separated. Teams should close that gap before agent usage scales further.

Discovery without remediation is insufficient for modern SaaS risk. The guide reflects a broader market shift away from passive inventory and toward active enforcement. That shift matters because data no longer sits in one repository long enough to be governed through periodic reviews alone. Security teams need controls that can act inside collaboration and AI workflows, especially where secrets, credentials, or regulated data are at stake. The practical conclusion is that posture tools and enforcement tools are converging requirements, not interchangeable options.

NHI governance now extends into AI data movement. AI agents are not human users, but they still consume permissions, retrieve information, and create audit obligations. That puts them squarely inside the NHI governance conversation, especially where access needs to be short-lived, attributable, and bounded by task. The field should stop treating AI agent data access as an adjacent problem and start treating it as an identity and privilege design issue. Practitioners should align AI controls with NHI lifecycle thinking.

The market is moving toward unified data control planes. Buyers are no longer comparing tools only on discovery depth. They are comparing whether one platform can span SaaS, cloud, browser, endpoint, and agentic AI workflows without leaving operational gaps between categories. That shift validates a broader governance approach, but it also complicates procurement because teams must assess integration depth, enforcement latency, and evidence generation together. Practitioners should re-evaluate whether their current stack answers the runtime question or only the inventory question.

What this signals

MCP visibility gap: the practical risk is not that AI agents exist, but that their retrievals can outrun audit and enforcement. Teams should expect more governance pressure around tool-call logging, data attribution, and policy enforcement as agentic workflows become normal in SaaS-heavy environments.

The strategic signal is that data security and identity governance are converging at the runtime layer. A programme that can classify sensitive data but cannot govern who or what is retrieving it will not satisfy either compliance teams or incident responders when AI usage expands.

If your current stack only sees storage and not movement, the next wave of risk will expose that limitation quickly. Security architecture needs to account for how AI agents interact with collaboration platforms, not just where the underlying data sits.

For practitioners

• Map runtime data flows across AI and SaaS systems Inventory where sensitive data moves after discovery, including Slack, Gmail, Salesforce, browser sessions, endpoint copy-paste, and MCP tool calls. Separate storage visibility from runtime enforcement so you can see which flows are currently ungoverned.
• Require inline remediation for high-risk data paths Prioritise controls that can redact, mask, tombstone, or block sensitive data in the workflow where it appears. If the platform only exports findings, pair it with an enforcement layer for collaboration and AI channels.
• Demand per-tool-call audit logging for AI agents Make tool-level logging a procurement requirement for MCP and agent integrations. Each retrieval should record the source system, policy decision, and downstream output so incident response and compliance teams can reconstruct behaviour later.
• Reassess whether DSPM alone covers your risk Use the evaluation to decide whether your programme needs discovery only or a combined DSPM and DLP model. Organisations with active SaaS and AI workflows should not assume cloud-only posture tools are sufficient.

Key takeaways

• The article shows that the main gap in Cyera alternatives is not discovery depth, but runtime control over data moving through SaaS and AI agent workflows.
• AI agents and MCP integrations create an auditability problem that traditional cloud DSPM cannot solve on its own.
• Practitioners should evaluate whether their controls can enforce policy inline, attribute each retrieval, and support incident reconstruction across the full data path.

Key terms

• Model Context Protocol: A protocol that lets AI agents connect to tools and data sources during runtime. In security terms, it creates a live retrieval path that can expose sensitive business data unless access, logging, and redaction are enforced at the tool-call boundary.
• Model Context Protocol: Model Context Protocol is an open protocol that lets AI agents connect to tools and data sources. It expands what an agent can reach, so governance has to cover not only the model and its prompts, but also every system that can receive or return agent-driven data.
• Runtime Data Remediation: A control pattern that changes data exposure in the moment of use rather than after discovery. It includes actions such as redaction, masking, tombstoning, blocking, and revocation inside the workflow where the sensitive data appears.
• AI Agent Auditability: The ability to reconstruct what an AI agent accessed, what tools it called, and what data it returned. Without this evidence, compliance, incident response, and access governance cannot verify whether the agent stayed within its intended scope.

What's in the full article

Strac's full buyer's guide covers the operational detail this post intentionally leaves for the source:

• Per-vendor comparison tables across SaaS DLP, DSPM, endpoint DLP, and AI agent MCP protection.
• Deployment and integration notes for Slack, Gmail, Google Drive, Salesforce, and other SaaS connectors.
• Product-level discussion of remediation actions such as redaction, masking, tombstoning, and blocking.
• Buyer criteria for evaluating time to value, OCR coverage, and compliance evidence generation.

👉 The full Strac guide includes the vendor comparison matrix, deployment notes, and MCP DLP coverage details.

Deepen your knowledge

The NHI Foundation Level course, the industry's only accredited NHI security programme, covers NHI governance, workload identity, secrets management, and agentic AI identity. It is designed for practitioners who need to connect identity controls to broader security programmes.