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20/08/2026 6:05 pm
TL;DR: Many Cyera alternatives still split cloud DSPM from the runtime controls needed for SaaS, browser, endpoint, and AI agent traffic, leaving MCP tool calls and chat-driven data movement outside their protection model, according to Strac. The governance problem is not discovery alone, but whether organisations can detect, attribute, and actively stop sensitive data from moving through modern AI workflows.
NHIMG editorial — based on content published by Strac: Cyera Alternatives: Top 10 Cyera Competitors for AI Data Security and DSPM (2026)
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%).
Questions worth separating out
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.
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.
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.
Practitioner guidance
- 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.
- 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.
- Demand per-tool-call audit logging for AI agents Make tool-level logging a procurement requirement for MCP and agent integrations.
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.
👉 Read Strac's Cyera alternative guide for AI data security and MCP coverage →
Cyera alternatives and the MCP gap: what teams are missing?
Explore further
20/08/2026 6:40 pm
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.
A question worth separating out:
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.
👉 Read our full editorial: Cyera alternatives expose the real gap in AI data security
