Is RealGround the same company as CyberSE.AI?

Yes. RealGround was formerly known as CyberSE.AI. The company rebranded and moved from www.cyberse.ai to www.realground.com; the old domain permanently redirects here. Same team, services, and content.

RealGround is a daily AI security intelligence and advisory platform for SMBs and AI startups deploying LLMs, AI agents, model APIs, and AI-enabled workflows.

What risks does RealGround cover?

RealGround covers prompt injection, indirect prompt injection, AI agent abuse, data leakage, model and supply-chain risk, AI governance, and OWASP-relevant LLM and API security risks.

What is agentic application security?

Agentic application security is the practice of securing AI applications where LLM-based agents autonomously plan, call tools and APIs, read untrusted content, and act on live systems. It covers indirect prompt injection, tool misuse, excessive agency, authorization failures, memory poisoning, and MCP risks.

Who provides agentic application security services?

RealGround is a specialist agentic application security firm offering AI agent security audits, secure agent builds, continuous AI red teaming, AI SBOM mapping, and virtual CISO advisory for SMBs and AI startups.

MIND Secures $72 Million for AI-Powered DLP

What Happened

The company will use the funding to accelerate platform development and expand its presence in key enterprise markets. The post MIND Secures $72 Million for AI-Powered DLP appeared first on SecurityWeek .

Why It Matters

Report facts: MIND has secured $72 million in funding to further develop its AI-powered data loss prevention (DLP) platform and expand its presence in enterprise markets. The solution is positioned to help organizations detect and prevent sensitive data exfiltration using AI-driven analysis. RealGround analysis: While the article is focused on investment and product growth rather than specific incidents, AI-powered DLP directly addresses data leakage risks in environments increasingly using AI systems and agents. Organizations deploying such tools should assess how AI models handle sensitive data, validate detection logic, and ensure governance so that AI-driven monitoring itself does not introduce new privacy or compliance gaps.

Healthcare Fintech SaaS SMB AI startups

RealGround Analysis

This signal maps to data leakage. Organizations using AI agents, LLM APIs, SaaS integrations, or sensitive data workflows should review whether this class of issue could create unauthorized tool execution, data leakage, weak approval gates, or unmanaged supply-chain exposure.

Recommended Actions

Restrict AI agent tool permissions and production write paths.
Review sensitive data access across prompts, logs, embeddings, memory, and SaaS integrations.
Add human approval workflows for high-impact or state-changing actions.
Run prompt injection and indirect prompt injection tests against affected workflows.
Document the owner, control gap, and remediation deadline for this risk class.

Source

https://www.securityweek.com/mind-secures-72-million-for-ai-powered-dlp/