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Multi agent AI systems coordinate autonomous agents that plan, delegate, and act across enterprise infrastructure without waiting for human approval. They represent a fundamental architectural shift from single-model AI deployments. That shift introduces security risks that existing controls were not designed to address, and as deployment accelerates, the risks compound. Organizations that build …

Windsurf is an AI-native code editor powered by an agentic engine called Cascade. Cascade breaks down multi-step coding tasks and delegates them to AI agents, while also connecting to external tools through Model Context Protocol (MCP) servers. Windsurf has documented vulnerabilities and exposures. Records include a critical flaw affecting all IDE versions, MCP vulnerabilities that ... Read more …

Banks are adopting AI faster than they are governing it. That creates operational, compliance, and security risks that legacy controls were never designed to manage. The gap between investment and governance is becoming a business issue. Banks need controls that address workforce governance, runtime defense, and autonomous-agent oversight before AI risk becomes operational loss or ... Read more »…

Agentic AI systems plan and act autonomously. They can query databases and call APIs, execute multi-step workflows, and delegate tasks to other agents at machine speed without a human checkpoint. That autonomy creates risks that many enterprises still struggle to fully see and govern. Agents trigger cascading actions across connected systems and operate through legitimate ... Read more » The post…

Healthcare organizations would never allow clinical decisions without oversight, yet that is what’s happening with AI. Across health systems, unsanctioned AI tools are already interacting with patient data, influencing decisions, and automating workflows without consistent oversight or auditability. The side effects span every layer of the organization and are already visible. Unsanctioned AI too…

Chatbot security testing covers red teaming, threat modeling, and runtime defense. Learn what a mature enterprise program requires before and after deployment. The post What Does Chatbot Security Testing Entail? appeared first on WitnessAI .

In most organizations today, employees are already pasting proprietary source code, customer records, or strategic plans into tools like ChatGPT, often through personal accounts outside enterprise control. Your security team likely has limited or fragmented visibility. For security leaders, this is a live data exposure event repeated with every unsanctioned prompt. The gap between workforce ... R…

DeepSeek’s AI models are already inside enterprise environments, deployed through open-source channels, running locally on laptops, and embedded in developer workflows that most security teams never review. Its rapid, largely ungoverned spread created compounding enterprise risk. Data flowed into the Chinese jurisdiction unchecked, while security tooling that wasn’t built to monitor conversationa…

Many enterprises deploying RAG do not yet have security controls that fully match the architecture. Retrieval-Augmented Generation connects large language models to live internal knowledge bases, giving AI direct access to financial records, customer data, internal policies, and proprietary research. That access is what makes RAG valuable, and what makes RAG security a distinct challenge ... Read…

AI agents are no longer experimental. They query databases, execute multi-step workflows, and take autonomous actions across enterprise systems, often without a human approving each step. That shift from generating text to executing actions fundamentally changes the enterprise risk equation. The systems responsible for authenticating and authorizing actors inside your environment were built for h…

Microsoft Copilot provides cross-application access to emails, files, chats, calendars, and meetings via Microsoft Graph, making it one of the most privileged AI deployments for enterprises. That level of access comes with serious risk. Copilot can instantly surface everything a user is permitted to see, and attackers can exploit this capability to extract sensitive data ... Read more » The post …

Enterprise AI is delivering real results. Two-thirds (66%) of organizations report productivity and efficiency gains from AI adoption. But the same systems driving those gains are creating new security challenges. As organizations embed AI deeper into critical operations, their exposure grows in ways that existing defenses weren’t designed to handle. AI agents are at the ... Read more » The post …

Zscaler architecture is rooted in web and proxy-based traffic inspection, which can limit visibility into certain AI interaction surfaces. However, enterprise AI now spans native desktop apps, developer IDEs, embedded copilots, and autonomous agents, many of which do not consistently route through a web proxy.  If your security strategy stops at the browser, you’re flying ... Read more » The post…

The way LLMs are designed and how they work opens up vulnerabilities and risks that traditional security tools were never built to handle. The consequences are already showing up across enterprises: proprietary data exfiltration, unauthorized actions triggered by manipulated model outputs, and compliance gaps that existing security stacks can’t close. 29% of cybersecurity leaders said ... Read mo…

Financial institutions operating in the EU must comply with DORA requirements as of 17 January 2025. The regulation defines clear expectations across: ICT risk management, third-party oversight, incident reporting, and resilience testing that function together as a unified control framework. But moving from regulatory text to operational reality is where many institutions encounter challenges. Tr…

Model Context Protocol (MCP) servers give AI agents autonomous access to enterprise databases, APIs, and internal systems. And they are proliferating rapidly, with 40% of enterprise apps expected to feature task-specific AI agents in 2026. However, MCP server security has not kept pace with that adoption, creating measurable gaps in enterprise visibility, control, and accountability. Below, we ex…

When most people hear “AI policy,” they think regulation, government frameworks, compliance mandates, and legal requirements. That’s a valid perspective, but it only focuses on the external dimension of AI policy enforcement. There’s also the AI enforcement layer, which focuses on AI use within your organization. The gap between written policy and operational enforcement is where AI risk lives. T…

Enterprise AI interactions often involve employees pasting sensitive data into third-party LLMs as part of their routine work. Without data tokenization or equivalent controls in place, that exposure compounds: the more value an organization captures from AI, the more sensitive data it puts at risk. By 2027, 40% of breaches will be caused by improper cross-border use of GenAI. This article explai…

Cursor AI security is a growing blind spot for enterprises, and the gap is widening fast. 84% of developers are now using or planning to use AI tools in their development process. Cursor is one of the fastest-growing options, but its usage is difficult to manage because it operates as a native desktop application rather than a browser-based tool. This guide breaks down why Cursor poses a distinct…