Defining Shadow AI and the New Visibility Gap
Shadow AI visibility is the ability for security and IT teams to detect, identify, and monitor unauthorized or unsanctioned AI tools, applications, and services used across enterprise endpoints, networks, and cloud environments so they can manage risk, enforce policy, and support responsible AI adoption. As employees adopt AI-powered applications, browser extensions, developer tools, APIs, and SaaS platforms on their own, these tools often sit outside standard procurement and governance processes. This activity creates a blind spot: traditional endpoint and cloud security tools rarely classify which AI models or services are in use, by whom, and for what purpose. A Gartner survey of cybersecurity leaders cited by N‑able found that 69% of organizations suspect or have evidence that employees are using prohibited public generative AI, underscoring how widespread the problem has become for enterprise security and compliance teams.
N-able’s Shadow AI Visibility: Monitoring Endpoint AI Usage
N‑able’s Shadow AI Visibility feature brings AI tool monitoring into its N‑central and N‑sight Unified Endpoint Management platforms and its Adlumin Security Operations environment. The capability identifies AI applications, browser extensions, developer tools, command-line interfaces, and AI-related network activity across managed endpoints without requiring new agents or consoles. By classifying tools by category, vendor, model family, and approval status, it helps teams build an accurate inventory of endpoint AI usage and align it with corporate policy. Identity and device attribution links specific users and systems to AI services, which is vital for audit trails and incident investigations. Integrated workflows let administrators query, report on, and respond to shadow AI usage from the tools they already use to manage fleets. For managed service providers, this visibility supports new services around AI governance, usage assessments, and compliance reporting for their customers.
Cloud Security Operations and AI-Aware SIEM on AWS
While N‑able focuses on endpoint AI usage, CrowdStrike and AWS are advancing shadow AI visibility inside cloud security operations. CrowdStrike’s Falcon AI Detection and Response (AIDR) delivers real-time inspection of AI agents, large language models, and Model Context Protocol communications in workloads built on services such as Amazon Bedrock and Strands Agents. This helps stop prompt injection, sensitive data leakage, and malicious AI activity at runtime, giving organizations continuous oversight of agentic workflows in production. Falcon Next‑Gen SIEM and Falcon Cloud Security extend that visibility into broader AWS telemetry via new Quick Start connectors for Amazon CloudWatch and Amazon S3 access logs. According to CrowdStrike, organizations can secure AI-powered applications on AWS with protections that span development, runtime, identities, and cloud infrastructure, while AWS PrivateLink cross‑region support reduces internet exposure when routing Falcon traffic across multi-account, multi-region environments.
Agentic AI Security Emerges as a New Category
The market is starting to recognize agentic AI security as a distinct discipline focused on monitoring, controlling, and securing autonomous AI agents across infrastructure. CrowdStrike’s designation as an inaugural AWS Agentic AI Specialization Partner signals that cloud platforms now treat AI agent security as more than a feature of general cloud protection. Cisco’s acquisition of WideField Security points in the same direction: large vendors are consolidating AI-native detection and response capabilities to watch AI systems as active entities, not just code artifacts. These moves reflect a shift from traditional rule-based controls toward continuous observation of agent behavior, model context, and non-human identities. As enterprises embed AI agents into business workflows and DevOps pipelines, dedicated agentic AI security tooling provides context-aware visibility, enabling teams to distinguish benign automation from risky or policy-violating AI activities across both on-premises and cloud environments.
Balancing AI Enablement with Compliance and Governance
Shadow AI adoption raises significant compliance and data governance risks because unapproved tools may move sensitive data into external services without logging or review. Traditional security tools rarely detect whether a browser plugin, code assistant, or SaaS chatbot is an AI service or how it handles data. New shadow AI visibility tools address this by giving organizations a clear inventory of AI tools in use, who uses them, and over which networks and workloads they operate. With that view, teams can define policy tiers—approved, conditionally allowed, or prohibited—and enforce them through endpoint controls and cloud security operations. N‑able’s integrated workflows and CrowdStrike’s AI-aware SIEM illustrate how AI enablement and compliance can coexist: organizations can allow high-value AI use cases while maintaining detailed monitoring, audit trails, and responsive controls that support regulatory requirements and responsible AI adoption.
