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.

SASE Has An AI Blind Spot. Inspecting Packets Is No Longer Enough.

What Happened

For years, routing traffic through cloud proxies was good enough. Then work moved to the browser, AI entered the workflow, and the inspection model stopped keeping up. Enterprise workflows now live across SaaS applications, browsers, and an expanding ecosystem of generative AI tools, unsanctioned browser extensions, and autonomous agents. Employees routinely paste intellectual property into

Why It Matters

The article describes how traditional SASE models that rely on inspecting network packets and web traffic are increasingly blind to AI-driven workflows that now occur inside SaaS apps, browsers, unsanctioned extensions, and autonomous agents.[4][11] It highlights that employees are routinely pasting sensitive IP and data into generative AI tools and browser-based agents where SASE and CASB controls have limited or no visibility.[4] From a RealGround perspective, this reflects a SaaS AI risk: organizations must augment network-layer controls with AI-aware monitoring and continuous red teaming of agentic workflows and browser-based AI usage to detect data leakage, shadow AI tools, and unsafe autonomous behavior across the full application and agent ecosystem.

Healthcare Fintech SaaS SMB AI startups

RealGround Analysis

This signal maps to SaaS AI risk. 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://thehackernews.com/2026/07/sase-has-ai-blind-spot-inspecting.html