AI Sprawl Requires Security Visibility and Governance

AI agents hold tremendous potential but security concerns remain a significant barrier to their full implementation. I found it interesting that the article highlights how Managed Cloud Platforms (MCP) can play a crucial role in addressing these security challenges for enterprises. What steps do you think organizations should take to enhance the security of their AI-driven workflows?

RSAC 2026 Recap: From AI Hype to Real SaaS Security Outcomes #AI It felt as though every single conversation, booth, and presentation at RSAC 2026 mentioned AI. But beyond the sheer volume of messaging around this topic, it is clear to us that a more important shift is taking place in the SaaS security sphere. Security teams are now moving beyond AI experimentation and asking harder questions. Everyone wants to know not only how to secure AI within SaaS environments, but also how AI improves security outcomes....

Reco Integrates Cyera DSPM to Deliver Complete Data-Aware SaaS and AI Security https://ow.ly/N3FH50YKlIi #TechnologyNews #AI #TechNews #CIOCommunity #CIOLeadership #CIOInfluence #TechLeadership #ITStrategy #FutureOfIT #TechTrends

AI enabled SaaS applications are no longer passive tools. They reason, act, and extend across systems in ways traditional security models were not built to handle. A shift toward structured, capability aware signals is essential for understanding how these applications behave and for enabling precise, consistent enforcement across modern security platforms.

In her insightful article, Yiwen Xu discusses how security concerns are hindering the advancement of AI agents and the role of managed cloud platforms (MCP) in addressing these challenges. I found it interesting that the security of AI solutions is still a major roadblock for many enterprises, and it highlights the need for robust solutions as we scale our workflows. What strategies have you implemented to enhance security in your AI initiatives?

SandboxAQ adds runtime guardrails, MCP risk analysis and cloud‑scanning to AQtive Guard AI‑SPM, aiming to tighten enterprise AI governance. Read the Latest Full News – https://lnkd.in/dDidmX-E #AIsecurity #AIspm #EnterpriseAI #AIguardrails #MCP #CloudScanning #RSA2026 #GenerativeAI #AIRegulation #SandboxAQ #AIrisk #TechEdgeAI #TechEdge

The rush to deploy AI agents is creating a critical blind spot. As this Forbes piece highlights, organisations are giving AI broad access to enterprise systems without the right oversight—creating a massive new security risk. AI agents need the exact same strict access controls as your human employees. At Google Cloud, we build these "least-privilege" guardrails natively into our security tools, allowing leaders to safely adopt AI without leaving the back door open. How is your team managing permissions for AI agents? https://lnkd.in/giEzZR3i

CrowdStrike announced new capabilities across the Falcon® platform that establish the endpoint as the epicenter for AI security and CrowdStrike as the market’s leading AI security platform. New platform innovations extend AI agent discovery, shadow AI governance, and runtime threat detection directly from the endpoint – the point of AI execution – to every surface where AI agents operate across SaaS, browser, and cloud environments. https://lnkd.in/gyidbRDQ

Your AI agents have valid credentials. Every API call passes authentication. Every permission check succeeds. But the agent just issued a $500 refund to a customer who made a minor complaint. The company's policy caps automatic refunds at $50. The API gateway saw a valid request. The IAM system saw a valid identity. Neither saw a policy violation. This is the agent runtime governance gap. Traditional IAM governs access to systems. It does not govern the decisions agents make. We wrote about what changes when you introduce Agent Runtime Governance as the control plane between the agent and the enterprise systems it acts on: Before ARG: If the credentials are valid, the action proceeds. No evaluation of intent, context, delegation chain, or business policy. After ARG: Every agent action is evaluated against enterprise policy before execution. Credentials are necessary but no longer sufficient. The post walks through the architecture, a concrete refund scenario, and why this follows the same pattern the industry has seen before: identity for cloud access, API gateways for microservices, and now runtime governance for AI agents. #AgentRuntimeGovernance #AISecurity #CISO #AgenticAI #EnterpriseAI #Cybersecurity #IAM #AIGovernance #WatchlightAI https://lnkd.in/drS48SfT

Your AI agents have valid credentials. Every API call passes authentication. Every permission check succeeds. But the agent just issued a $500 refund to a customer who made a minor complaint. The company's policy caps automatic refunds at $50. The API gateway saw a valid request. The IAM system saw a valid identity. Neither saw a policy violation. This is the agent runtime governance gap. Traditional IAM governs access to systems. It does not govern the decisions agents make. We wrote about what changes when you introduce Agent Runtime Governance as the control plane between the agent and the enterprise systems it acts on: Before ARG: If the credentials are valid, the action proceeds. No evaluation of intent, context, delegation chain, or business policy. After ARG: Every agent action is evaluated against enterprise policy before execution. Credentials are necessary but no longer sufficient. The post walks through the architecture, a concrete refund scenario, and why this follows the same pattern the industry has seen before: identity for cloud access, API gateways for microservices, and now runtime governance for AI agents. #AgentRuntimeGovernance #AISecurity #CISO #AgenticAI #EnterpriseAI #Cybersecurity #IAM #AIGovernance #WatchlightAI