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Stay updated with the latest cybersecurity trends, threat analyses, compliance guides and best practices from our experts.
AI systems introduce new challenges for security investigations. Learn how AI Forensics helps organizations investigate Prompt Injection, data leakage, compromised AI accounts, RAG incidents, AI agent misuse, API abuse, and Shadow AI by tracing prompts, data, identities, connectors, and AI actions.
AI SaaS applications are rapidly becoming part of everyday enterprise workflows, but they also introduce new risks involving sensitive prompts, OAuth permissions, Shadow AI, AI agents, connectors, RAG systems, third-party models, and persistent access to enterprise data. This guide explains how organizations can build a practical AI SaaS Security program covering discovery, vendor assessment, IAM, AI DLP, access control, integration security, monitoring, governance, and incident response.
AI meeting assistants such as Otter, Fireflies, and other AI transcription tools can improve productivity but also create new enterprise security risks. This guide explains AI Meeting Assistant Security, including Shadow AI, transcript exposure, OAuth permissions, recording controls, data retention, AI DLP, connector security, vendor risk, continuous monitoring, and best practices for protecting sensitive enterprise conversations.
Local AI can reduce some external data exposure risks, but running Ollama, LM Studio, and private LLMs inside the enterprise does not automatically make AI secure. This guide explains Local AI Security, including model provenance, supply-chain risks, insecure local APIs, Shadow Local AI, RAG security, secrets exposure, network segmentation, endpoint hardening, AI DLP, MCP and connector security, continuous monitoring, and lifecycle governance for secure private AI deployments.
As AI agents gain access to enterprise applications, APIs, cloud platforms, databases, MCP servers, and sensitive data, Non-Human Identity Security is becoming critical. This guide explains how organizations can secure AI agent identities using least-privilege access, workload identities, short-lived credentials, secrets management, Zero Trust, Privileged Access Management, and continuous monitoring to reduce identity-based risks and safely manage autonomous AI systems.
AI connectors enable enterprise AI systems, agents, RAG applications, MCP servers, and LLM platforms to interact with business applications, APIs, databases, SaaS tools, and sensitive data. This guide explains AI Connector Security, including authentication, least-privilege access, OAuth and token security, AI DLP, Prompt Injection protection, RAG authorization, MCP Security, AI Agent tool controls, third-party risk, continuous monitoring, and lifecycle governance for secure enterprise AI integrations.
AI Gateway Security provides a centralized control layer for managing traffic between employees, applications, AI agents, LLMs, APIs, RAG systems, MCP connectors, and enterprise data. This guide explains how organizations can use AI gateways to enforce authentication, access control, prompt security, AI DLP, model routing, threat detection, rate limiting, logging, and continuous monitoring while reducing Shadow AI and enterprise AI security risks.
An AI Risk Register helps enterprises systematically identify, assess, prioritize, assign, mitigate, and continuously monitor risks across generative AI, LLMs, RAG systems, AI agents, MCP connectors, and third-party AI platforms. This guide explains how to build and manage an enterprise AI Risk Register using risk scoring, ownership, treatment plans, security controls, continuous monitoring, and AI governance best practices.
As artificial intelligence becomes embedded across enterprise workflows, organizations need continuous visibility into how employees, AI agents, browser extensions, AI coding assistants, and third-party AI platforms are being used. AI Usage Monitoring helps detect Shadow AI, monitor unsafe AI behavior, identify policy violations, protect sensitive data, and support enterprise AI governance. This guide explains AI Usage Monitoring, Shadow AI detection, AI User Behavior Analytics, governance strategies, AI Security Monitoring, compliance best practices, and how organizations can securely scale AI adoption while reducing cyber risk.
As enterprises increasingly integrate ChatGPT, Microsoft Copilot, Claude, Gemini, and other Large Language Models into daily operations, prompts have become one of the most valuable—and overlooked—sources of sensitive information. Employees often include confidential business data, customer records, source code, financial information, legal documents, and strategic plans within AI prompts to receive better responses. Without proper security controls, this information can unintentionally be exposed through AI applications, third-party integrations, browser extensions, prompt history, logging systems, or compromised AI workflows. This guide explains Prompt Leakage, its causes, attack vectors, business impact, prevention strategies, AI Data Loss Prevention (AI DLP), governance best practices, and how enterprises can protect sensitive information while safely adopting generative AI.
As AI agents become more capable, the Model Context Protocol (MCP) is emerging as a standard for connecting Large Language Models (LLMs) with enterprise applications, APIs, databases, file systems, and business tools. While MCP enables powerful AI workflows, it also introduces new security challenges related to authentication, authorization, prompt injection, tool abuse, data leakage, privilege escalation, and third-party integrations. This guide explains MCP Security, the enterprise MCP architecture, common attack vectors, best practices, governance strategies, and how organizations can securely deploy MCP connectors at scale.
AI coding assistants such as GitHub Copilot, Cursor, Claude Code, and Windsurf are transforming software development by accelerating code generation, debugging, documentation, and application delivery. However, these tools also introduce new risks, including source code leakage, insecure code generation, prompt injection, excessive repository access, credential exposure, malicious dependencies, and software supply chain compromise. This guide explains how enterprises can implement AI Coding Assistant Security through strong governance, least-privilege access, secure development practices, code validation, monitoring, and AI-specific security controls.
AI-powered browser extensions are transforming workplace productivity by integrating ChatGPT, Microsoft Copilot, Gemini, Claude, and other AI assistants directly into web browsers. However, these tools can introduce significant cybersecurity risks, including data leakage, credential theft, excessive permissions, malicious extensions, prompt injection, browser-based attacks, and compliance challenges. This guide explores AI Browser Extension Security, common attack vectors, enterprise risks, security best practices, governance strategies, and how organizations can safely adopt AI-powered browser tools.
As organizations rapidly adopt Microsoft Copilot, ChatGPT Enterprise, Claude, and Google Gemini, securing these AI platforms has become a business priority. This comprehensive guide explores Enterprise AI Security best practices, including AI governance, identity and access management, data protection, API security, AI Security Operations (AI SecOps), continuous monitoring, and compliance. Learn how to reduce AI-related cyber risks, prevent data leakage, secure enterprise AI integrations, and confidently scale generative AI across your organization while maintaining security, privacy, and regulatory compliance.
As enterprises rapidly adopt Artificial Intelligence, understanding the maturity of AI security capabilities has become essential for reducing cyber risk and ensuring long-term resilience. An AI Security Maturity Assessment helps organizations evaluate governance, AI risk management, security architecture, operational controls, AI Security Operations (AI SecOps), monitoring, compliance, and continuous improvement across the entire AI ecosystem. This comprehensive guide explains how to measure enterprise AI readiness using a structured maturity model, identify security gaps, benchmark organizational capabilities, and build a roadmap for secure, scalable, and compliant AI adoption.
Enterprise AI applications rely on APIs to connect Large Language Models (LLMs), AI agents, Retrieval-Augmented Generation (RAG) systems, and business applications. While these integrations enable powerful automation and intelligence, they also introduce significant security risks. This comprehensive guide explains AI API Security, covering authentication, authorization, API gateways, prompt protection, monitoring, AI API abuse prevention, incident response, and enterprise best practices to help organizations securely deploy and manage AI integrations.
Artificial Intelligence is transforming enterprise operations, but securing AI requires more than a one-time assessment. AI Security Operations (AI SecOps) provides a continuous approach to monitoring, detecting, responding to, and mitigating AI-specific threats across Large Language Models (LLMs), AI agents, APIs, Retrieval-Augmented Generation (RAG) systems, and enterprise integrations. This comprehensive guide explains how organizations can build an AI SecOps strategy using AI security monitoring, threat detection, telemetry, incident response, automation, governance, and continuous risk management to strengthen enterprise AI resilience.
As enterprises increasingly adopt AI, continuous security monitoring has become essential for detecting emerging threats and protecting sensitive data. This comprehensive guide explains how AI Security Monitoring uses logging, AI telemetry, SIEM integration, Security Operations Centers (SOC), anomaly detection, and incident response to identify prompt injection attacks, AI agent abuse, data leakage, Shadow AI, RAG security risks, and other AI-specific threats. Learn best practices, implementation strategies, and key metrics to build a resilient AI monitoring program for enterprise AI systems.
Enterprise AI introduces new security challenges across AI models, RAG systems, AI agents, APIs, vector databases, and third-party services. This guide explains how AI Attack Surface Management helps organizations continuously discover hidden AI assets, assess security exposures, prioritize risks, and reduce the enterprise AI attack surface before attackers can exploit it.
AI Threat Modeling enables organizations to identify, prioritize, and mitigate security risks before deploying enterprise AI systems. Learn how to secure LLMs, RAG applications, AI agents, and AI architectures using proven threat modeling methodologies, STRIDE, and MITRE ATLAS.
Employees are pasting sensitive data into ChatGPT, Claude and Copilot every day. Here is how to discover and govern shadow AI usage.