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Non-Human Identity Security for AI Agents: Managing Machine Identities and Access

 Artificial Intelligence is introducing a new category of enterprise identity risk. Organizations traditionally designed Identity and Access Management (IAM) around employees, administrators, contractors, and other human users. Modern enterprise environments, however, contain a rapidly growing number of machine identities associated with applications, APIs, service accounts, cloud workloads, automation platforms, bots, and AI agents. These identities are known as Non-Human Identities (NHIs) . As organizations deploy autonomous and semi-autonomous AI agents, Non-Human Identity Security is becoming increasingly important. AI agents need authenticated access to enterprise systems to perform useful work. An agent may retrieve information from a database, interact with a CRM platform, access internal documents, call APIs, use SaaS applications, execute automated workflows, or communicate with other AI systems. Each capability requires some form of identity and authorization. The securit...
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AI Connector Security: Managing Risks in Enterprise AI Integrations

 Enterprise AI systems are becoming increasingly connected to real business applications and sensitive organizational data. Large Language Models (LLMs), AI agents, RAG applications, enterprise copilots, and AI assistants can now interact with document repositories, CRM systems, databases, APIs, cloud platforms, email, source-code repositories, SaaS applications, and other enterprise services. These connections significantly increase the usefulness of AI, but they also expand the enterprise attack surface. AI Connector Security is the practice of protecting the integrations that allow AI systems to interact with enterprise applications, data, APIs, tools, and external services. The security challenge extends beyond ensuring that an API is authenticated or encrypted. Organizations also need to understand which identity the AI connector uses, what permissions it receives, what information it can access, what actions it can execute, and whether the AI should be allowed to invoke that...
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AI Gateway Security: How to Control and Secure Enterprise AI Traffic

 Enterprise Artificial Intelligence environments are becoming more complex. Organizations may simultaneously use Large Language Models (LLMs), AI copilots, AI agents, RAG applications, coding assistants, AI APIs, browser-based AI tools, MCP connectors, and multiple third-party model providers. Every interaction between these technologies creates AI traffic that may contain sensitive business information. Traditional security controls remain important, but AI introduces additional challenges. Prompts may contain confidential information, AI-generated responses may expose sensitive data, AI agents may invoke enterprise tools, RAG systems may retrieve restricted documents, and employees may access unauthorized AI services. Organizations therefore need greater visibility and control over how AI traffic moves across the enterprise. An AI Gateway provides a centralized control layer between employees, enterprise applications, AI agents, and AI models. It enables organizations to apply c...
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AI Risk Register: Building and Managing Enterprise AI Risk

 As organizations deploy more Artificial Intelligence systems, managing AI risk becomes increasingly complex. Enterprise environments may include generative AI applications, Large Language Models (LLMs), RAG systems, AI agents, coding assistants, AI APIs, browser extensions, third-party platforms, and Model Context Protocol (MCP) connectors. Each technology can introduce different cybersecurity, privacy, compliance, operational, data, and governance risks. An AI Risk Register provides organizations with a structured way to identify, assess, prioritize, assign, mitigate, and continuously monitor these risks. Unlike a traditional vulnerability list, an enterprise AI Risk Register should explain the complete business context surrounding each risk. A useful risk entry identifies the affected AI system, describes what could go wrong, evaluates likelihood and impact, records existing security controls, assigns an accountable owner, defines remediation activities, and tracks the remainin...
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Enterprise AI Usage Monitoring: Detecting Shadow AI and Unsafe AI Behavior

 Artificial Intelligence has quickly become part of everyday enterprise workflows. Employees use AI assistants to generate content, summarize documents, analyze information, write code, conduct research, and automate repetitive tasks. Organizations are also deploying AI copilots and autonomous agents across business functions. However, rapid AI adoption creates an important security challenge: How can organizations understand what employees and systems are actually doing with AI? Approved enterprise AI platforms represent only part of the environment. Employees may independently use public AI chatbots, browser extensions, coding assistants, productivity tools, and other AI services without informing security or IT teams. This unauthorized or unmanaged use of artificial intelligence is commonly referred to as Shadow AI . Shadow AI can introduce serious cybersecurity and compliance risks. Employees may unknowingly share customer information, internal documents, credentials, intellect...
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AI Browser Extension Security: Hidden Risks of AI-Powered Browser Tools

 AI-powered browser extensions are rapidly changing how employees work online. From summarizing webpages and drafting emails to generating code and answering questions, these extensions make AI accessible directly within the browser. However, while they improve productivity, they also introduce significant cybersecurity risks that organizations cannot ignore. Unlike traditional browser extensions, AI-powered tools often require broad permissions to analyze webpage content, interact with browser tabs, access clipboard data, process uploaded documents, and communicate with cloud-based AI services. These permissions may expose sensitive enterprise information if they are not properly controlled. AI Browser Extension Security focuses on identifying, assessing, and reducing the risks associated with AI-enabled browser tools across the enterprise. A comprehensive security strategy begins with visibility. Organizations should maintain an inventory of approved browser extensions, identify...
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AI Security Operations (AI SecOps): Building a Continuous AI Defense Strategy

 Artificial Intelligence is becoming a core part of enterprise operations. Businesses are deploying Large Language Models (LLMs), AI agents, Retrieval-Augmented Generation (RAG) applications, AI copilots, and intelligent automation to improve productivity and accelerate decision-making. However, as AI adoption grows, organizations also face new cybersecurity challenges that require continuous protection rather than periodic security reviews. Traditional security operations were designed to monitor networks, endpoints, cloud workloads, and applications. AI introduces an entirely new attack surface with risks such as prompt injection, model abuse, unauthorized access, excessive permissions, insecure APIs, Shadow AI, data leakage, and compromised AI agents. These threats continue to evolve long after AI systems are deployed. AI Security Operations (AI SecOps) is the practice of continuously monitoring, detecting, investigating, and responding to security threats targeting enterprise ...
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AI Security Monitoring: Detecting Threats in Enterprise AI Systems

 Artificial Intelligence is becoming an essential part of enterprise operations. Organizations are deploying AI assistants, Large Language Models (LLMs), AI agents, Retrieval-Augmented Generation (RAG) applications, and cloud AI services to improve productivity and automate decision-making. While these technologies deliver significant business value, they also introduce new security risks that require continuous monitoring. Unlike traditional software, AI systems constantly process prompts, generate responses, access enterprise knowledge, communicate with external APIs, and interact with sensitive business data. These dynamic behaviors create opportunities for attackers to exploit vulnerabilities long after an AI application has been deployed. AI Security Monitoring is the continuous process of observing AI systems, detecting abnormal activity, identifying potential threats, and responding to security events before they impact business operations. A comprehensive AI Security Monit...
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AI Attack Surface Management: Discovering Hidden AI Risks Before Attackers Do

 Artificial Intelligence is becoming deeply integrated into modern enterprises. Organizations are deploying AI copilots, AI agents, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) applications, cloud AI services, APIs, and intelligent automation across multiple business functions. While these technologies improve efficiency, they also introduce new security challenges. Every AI model, API, vector database, AI agent, third-party integration, cloud workload, and enterprise data connection expands the organization's attack surface. Without complete visibility into these assets, security teams may overlook exposures that attackers can exploit. AI Attack Surface Management (AI ASM) helps organizations continuously discover and manage AI-related assets across their environment. Unlike traditional point-in-time security assessments, AI ASM continuously identifies AI components, monitors configuration changes, tracks exposed services, detects unauthorized AI deployments...
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AI Threat Modeling: How to Identify Security Risks Before Deploying Enterprise AI

 Artificial Intelligence is transforming the way organizations operate, but every AI deployment introduces new security challenges. Large Language Models (LLMs), AI agents, Retrieval-Augmented Generation (RAG), APIs, vector databases, and cloud infrastructure create an expanded attack surface that traditional security assessments often fail to address. AI Threat Modeling helps organizations identify and mitigate these risks before deployment. Threat modeling is a structured process that analyzes an AI system's architecture to understand how attackers could exploit weaknesses. Instead of waiting for security incidents to occur, organizations evaluate potential attack scenarios during the design phase and implement appropriate safeguards. A comprehensive AI Threat Modeling exercise typically reviews data flows, trust boundaries, user interactions, AI models, external integrations, APIs, identity controls, and infrastructure components. Security teams identify threats such as prompt ...
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