Companies and employees are racing to capture the value and efficiencies offered by AI, but security is often an afterthought. Employees are using unauthorized GenAI tools to summarize documents, draft emails, and analyze potentially sensitive or proprietary data. Developers are adding AI capabilities before security teams can review them. SaaS platforms are adding AI features that may process sensitive business data by default. The result is a new attack surface expanding faster than most organizations can govern. For CISOs and CIOs, the challenge is twofold. You must secure how employees use AI in daily work, and you must protect the AI-enabled applications your organization is building and consuming. Without visibility across both, shadow AI becomes a blind spot where data can move, policies can fail, and adversaries can operate with less resistance. Shadow AI goes beyond employees pasting content into public chatbots. It includes unapproved AI assistants, embedded copilots inside SaaS applications, unapproved AI features, and internally developed AI workflows that bypass governance. Many organizations lack a unified view of where AI is being used, the data being exposed, or where or how to apply controls. Security teams are left unable to answer basic, yet critical, questions: Which AI services are employees accessing? What sensitive data is being shared? Are developers connecting proprietary code or customer data to external models? As the uncertainty increases, so do the risks of data leakage, compliance failures, inconsistent policy enforcement, and reputational damage. Enterprises face new AI-specific attacks. For example, prompt injection techniques can manipulate models into exposing information, ignoring safeguards, or taking unintended actions. Indirect prompt injection is especially dangerous because malicious instructions may be hidden in trusted sources such as documents, websites, or knowledge bases. Prompt injection is a broad and rapidly evolving threat landscape that warrants dedicated attention. For a deeper exploration of how these attacks are defined and categorized, we recommend reviewing our comprehensive overview: Prompt Injection: Definition and Attack Taxonomy. Traditional security tools were built for a different era defined by network perimeters, known attack signatures, and human-driven interactions. They were never designed to interpret the intent or content of AI interactions. Web proxies and firewalls cannot inspect encrypted traffic. Locally running AI applications may operate entirely on the endpoint and generate no network telemetry. Zero Trust and network segmentation, while foundational to modern security strategies, were built around human-to-system interactions — not the emerging reality of agent-to-agent and agent-to-tool communications, where autonomous AI systems make access decisions at machine speed, outside the reach of traditional policy enforcement. Perhaps most importantly, while Zero Trust can govern which data a user is permitted to access directly, it cannot control which data becomes accessible through an LLM via retrieval, tool calls, or agentic workflows acting on the user's behalf. That is a fundamentally different problem, and one that conventional architectures were never designed to solve. The result is a dangerous gap between existing security coverage and emerging AI risk. Organizations may have strong controls across endpoint, identity, and cloud, and still miss the moment sensitive data is exposed through a GenAI tool, or when an AI workflow is manipulated through malicious input. Closing that gap requires a purpose-built approach. CrowdStrike Falcon® AI Detection and Response (AIDR) is designed to provide the visibility, control, and protection that AI-driven environments demand. It can identify and stop AI-specific threats such as prompt injection, data leakage, and credential abuse targeting AI services, before they become breaches. Where traditional tools see infrastructure, CrowdStrike sees the full picture: which AI is being used, which data and prompts are reaching those systems, and whether the interactions represent risk. By unifying protection across endpoint, identity, cloud, and AI on a single platform, CrowdStrike enables security teams to defend AI-powered applications with confidence and reduce risk without slowing the business. First, assess shadow AI exposure by identifying which AI tools are in use, where AI features are enabled in SaaS applications, and which sensitive data is already flowing to those services. Second, define governance that matches real usage. Establish approved tools, acceptable use policies, and review processes for AI applications and integrations before they reach production. Third, deploy integrated controls to prevent access or data egress to unauthorized AI services, detect prompt injection and AI-related abuse, and monitor for adversary activity across identity, cloud, and endpoint. AI creates real business value, but without visibility and control, it expands the attack surface in ways traditional security wasn’t built to handle. Shadow AI cannot be left unmanaged, and fragmented tools cannot keep pace with how quickly AI is being adopted across the enterprise. CrowdStrike unifies AI visibility, control, and protection on a single platform built for how AI is used in the modern enterprise. Security teams gain the insight they need, and the business keeps moving.Shadow AI Is Bigger Than Unauthorized Chatbots
AI-Native Threats Are Already Here
Why Traditional Security Falls Short
Three Actions to Take Now
Turn AI into an Advantage
Additional Resources
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