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.

Bedrock’s ArgusAI Offers Transparency into Data Access by AI Models and Agents

What Happened

Bedrock launched ArgusAI, a capability that gives organizations direct transparency into what data AI models and agents are accessing during training and inference.[2] This is aimed at mitigating data leakage and unauthorized data exposure risks in LLMs and agentic systems used by SaaS providers, fintechs, and other data‑sensitive startups.[2]

Why It Matters

The article reports that Bedrock’s ArgusAI gives organizations visibility into what data AI models and agents access during training and inference, with the stated goal of reducing sensitive-data exposure and unauthorized access. It is positioned for data-sensitive environments such as SaaS, fintech, and other regulated or high-trust use cases. From a RealGround perspective, this is primarily a data leakage concern because the control problem is understanding and limiting what data agents can surface or expose.

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://theaiinsider.tech/2026/05/21/the-top-15-ai-cybersecurity-scale-ups-you-need-to-know-in-2026/