Cyera alternatives expose the real gap in AI data security

At a glance

What this is: This is a buyer's guide arguing that the main weakness in many Cyera alternatives is the gap between cloud DSPM and active data controls across SaaS, endpoints, browsers, and MCP-connected AI agents.

Why it matters: It matters because identity and data governance teams increasingly need control at the point of use, not just visibility at rest, especially as AI agents move sensitive data through tool calls and SaaS workflows.

Context

Cloud DSPM can show where sensitive data exists, but it does not automatically govern how that data moves through SaaS, browsers, endpoints, and AI-mediated workflows. That gap matters because the control problem has shifted from inventory alone to runtime enforcement across multiple trust boundaries, including the identity and authorisation paths used by AI agents.

In practical terms, the article frames MCP as the governance fault line. When AI agents read from SaaS systems and push content into model context windows, security teams need controls that see the data movement itself, not just the source repository or cloud bucket.

Key questions

Q: What breaks when AI data security stops at cloud DSPM?

A: Teams can see where data lives, but they cannot stop the same data from flowing into SaaS chats, browser sessions, endpoint copy-paste, or AI agent tool calls. That creates a false sense of coverage because the policy boundary sits too far upstream. Runtime enforcement has to move to the point where the data leaves the original system.

Q: Why do MCP-connected agents increase AI data leakage risk?

A: MCP-connected agents can retrieve data directly from enterprise systems, so sensitive information may enter AI workflows without a person copying it into a prompt. That creates a delegated retrieval path, which expands the attack and compliance surface. Organisations need policy controls on what the agent may retrieve and what may be passed onward to the model.

Q: How should security teams decide between remediation and mitigation?

A: Start with exploitability, asset criticality, and deployment feasibility rather than severity alone. If a flaw is actively exploited and the asset is business critical, remediation should usually be accelerated. If patching is unavailable or too disruptive, use mitigation as a time-bounded control with clear ownership and a scheduled path to permanent fix.

Q: How should teams govern AI agent access to production data?

A: Treat agent actions as runtime events that need policy checks at execution time, not just pre-approved credentials. High-risk reads, writes, and deletes should be bounded by context, session limits, and approval gates that apply before the command completes.

Technical breakdown

Why cloud DSPM stops short of runtime data control

DSPM is built to discover, classify, and score sensitive data in cloud repositories such as object storage, warehouses, and databases. That works well for answering where data lives, but it does not stop a message, file, or prompt from carrying the same data into another system. Runtime data control adds enforcement at the moment of transfer, such as redaction, masking, blocking, or quarantine. In AI-heavy environments, that distinction is crucial because the security decision is made at the boundary, not after the fact.

Practical implication: Treat DSPM as visibility infrastructure and pair it with enforcement where data leaves the system of record.

Why MCP changes the data security boundary

MCP, the Model Context Protocol, gives AI agents a standard way to connect to external tools and data sources. That makes tool-call payloads part of the security surface, because the agent can retrieve sensitive content from SaaS systems and pass it into the model context. Traditional DLP tools often inspect endpoints or storage, but not the protocol-level exchange between the agent and the application. Once that exchange is in play, governance has to follow the request path, the returned content, and the downstream context window.

Practical implication: Instrument MCP-connected workflows so policy can inspect and constrain the payload before it reaches the model.

Why SaaS, browser, and endpoint controls must be treated as one policy plane

The article's core architectural point is that data loss now happens across several user and machine interfaces, not in a single channel. A user pasting into ChatGPT, an AI agent pulling from Salesforce, and a file moving through Slack all create different but related leakage paths. A fragmented control model leaves blind spots where the same data class is governed differently depending on where it appears. The better model is a single control plane with consistent policy, detection, and response logic across all of those surfaces.

Practical implication: Unify policy across SaaS, browser, endpoint, and AI agent surfaces so enforcement does not depend on the channel.

Threat narrative

Attacker objective: The objective is to move sensitive business, personal, or regulated data into AI workflows or third-party tools without effective runtime inspection or blocking.

1. Entry occurs when an AI agent or user session accesses sensitive content through SaaS applications, browser activity, or endpoint copy-paste workflows.
2. Credentialed access then carries that data into MCP tool calls or chat interfaces, where the payload is exposed to model context or another downstream system.
3. Impact is data disclosure or policy bypass, because discovery alone cannot stop the transfer once the content has already left the original control boundary.

Breaches seen in the wild

• SalesBleed Salesforce Agentforce 2026: Three fixed Agentforce flaws let poisoned web leads make AI agents leak CRM data with zero clicks and send phishing under the agent's identity.
• CoPhish OAuth phishing via Copilot Studio: Datadog showed Copilot Studio agents on a Microsoft domain can front OAuth consent phishing and forward stolen tokens; no victims reported.

Read and download The State of NHI & AI Agent Breach Report 2026, covering 200+ breaches impacting Non-Human Identities including AI Agents.

NHI Mgmt Group analysis

MCP DLP is becoming the new control boundary for AI data security: Cloud discovery answers where sensitive data resides, but it does not govern how AI agents move that data into context windows or downstream tools. The article is really describing a shift from repository-centric security to transaction-centric security, where the policy decision has to happen at the tool-call boundary. Practitioners should now treat MCP as a first-class data governance surface.

Discovery without enforcement creates governance debt: Many programmes still measure success by coverage of cloud stores, but modern leakage paths happen in Slack, Gmail, browsers, and agentic workflows. That creates a mismatch between what teams can see and what they can stop. The result is governance debt: visibility accumulates faster than the ability to act on it. Teams need policy that can redact, block, or tombstone in motion, not only classify at rest.

Identity-aware data control now extends to AI agents as intermediaries: When an AI agent retrieves data on behalf of a user, the control question is no longer just whether the human is entitled to the data. It is also whether the agent, connector, and session are authorised to move it into a new trust zone. That is an identity and privilege problem as much as a data problem, and it belongs in access governance as well as DLP.

Runtime redaction is the practical answer to AI-assisted leakage: A static compliance posture does not help when PHI, PCI, secrets, or source code are already in motion across SaaS and GenAI surfaces. The article points to an operational model where detection is paired with inline remediation. Practitioners should use that model to reduce exposure before data reaches an AI system, not after the fact.

Stronger AI data security now depends on policy consistency across surfaces: The same sensitive record can be governed one way in cloud storage, another way in SaaS, and not at all in a browser session. That inconsistency is where leakage happens. Security leaders should define one policy model for the full data path, because the adversary does not respect product boundaries.

From our research library:

• Business leaders plan to spend $124 million on average on AI in 2026, and 91% say data security and risk will shape their AI strategy.
• Read next: AI Agent Authorisation Guide

What this signals

MCP DLP is emerging as the missing control plane for AI-heavy data environments: Once sensitive content can move from SaaS into model context windows, teams need enforcement that understands the protocol and the payload, not just the storage location. This is where identity, authorisation, and DLP converge into one governance problem.

Governance programmes should expect data security to shift from static discovery to dynamic control: The operational question is no longer whether data is classified, but whether it can be stopped in motion when a human or AI workflow tries to move it across boundaries. That change will reshape how practitioners think about DLP architecture, especially in mixed SaaS and AI estates.

For practitioners

• Define the AI data movement boundary Map where sensitive data leaves cloud repositories and enters SaaS, browser, endpoint, and AI agent workflows. Use that map to identify the channels where discovery exists but enforcement does not.
• Add policy at the MCP tool-call layer Inspect MCP-connected requests and responses so redaction, masking, or blocking happens before the model receives regulated or sensitive content.
• Unify SaaS and endpoint DLP rules Apply the same data-class policy across Slack, Gmail, Salesforce, browser copy-paste, and endpoint file transfers so one channel does not become the exception.
• Prioritise remediation over inventory Treat detection findings as incomplete until the workflow can actively stop or alter the transfer, especially for PHI, PCI, credentials, and source code.
• Review AI agent authorisation separately from human access Check whether the connector, session, and delegated workflow have a narrower privilege scope than the user who triggered the action.

Key takeaways

• Many Cyera alternatives still solve discovery better than control, which leaves the real exposure path in SaaS, browser, endpoint, and AI agent traffic.
• The MCP tool-call layer is now a governance boundary because it can move sensitive data from approved systems into model context windows.
• Practitioners should evaluate AI data security platforms on runtime enforcement, delegated access scope, and policy consistency across the full data path.

Key terms

• MCP DLP: MCP DLP is the protection of sensitive data that moves through Model Context Protocol connections between AI agents and external tools or data sources. It combines content inspection, policy enforcement, and access controls to prevent leakage of secrets, personal data, regulated records, or prompts during agent tool use and context exchange.
• Runtime Data Access: Runtime data access is the live retrieval of production data by an AI system while it is operating. It is the highest-risk governance stage because sensitive information can be exposed, transformed, or acted on at machine speed before human review can intervene.
• AI security control plane: The collection of identities, permissions, data paths, and response actions that an AI-enabled security stack depends on. It becomes a control plane when model output can influence operational decisions, making access governance, auditability, and revocation part of the security design.
• Delegated Access: Delegated access is permission granted to one identity to act on behalf of another user, service, or system. In NHI environments, this usually appears in OAuth-connected apps and automation tooling. It is powerful, but it must be tightly scoped and reviewed because it can persist long after the original business need ends.

Deepen your knowledge

NHI Foundation Level course, the industry's only accredited NHI security programme, covers NHI governance, agentic AI identity, and workload identity for teams building real-world access controls. It is designed for practitioners who need to govern machine-mediated access alongside human identity programmes.