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# You Already Have the Stack: Detecting Shadow AI and Living Off the AI with Your Existing Security Controls
- URL: https://incentivegradient.com/you-already-have-the-stack-detecting-shadow-ai-and-living-off-the-ai-with-your-existing-security-controls/
- Published: 2026-09-27T16:05:15.000Z
- Updated: 2026-09-27T16:05:15.000Z
- Author: Tim Rains

***The gap isn’t tools, it’s configuration.***

### **Executive Summary**

Enterprises do not need new AI‑security platforms to detect Shadow AI or LOTAI. They need to configure the controls they already operate. Shadow AI emerges when AI systems run without security team knowledge; LOTAI emerges when attackers repurpose sanctioned AI systems using valid credentials. Both threat surfaces already generate actionable signals across XDR, SIEM/XSIAM, IAM, DLP, CASB, cloud workload security, and vulnerability management, provided those controls are configured for AI‑specific telemetry. This framework outlines how each component delivers discovery, behavioral monitoring, and runtime protection, enabling CISOs to establish full operational visibility using the stack they already own.

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Enterprises don’t need new AI‑security platforms to detect Shadow AI or LOTAI. They need to configure the controls they already have. The rapid adoption of AI systems has created both opportunity and risk for CISOs, but the most urgent challenge is visibility: detecting unauthorized AI usage before it becomes a governance failure or an adversary advantage.

Unauthorized AI activity in the enterprise falls into two distinct categories:

**1\. Shadow AI**

Shadow AI is the unauthorized and ungoverned use of artificial‑intelligence tools, models, agents, or embedded AI features inside an organization — typically without the knowledge or approval of IT, security, or compliance teams. This includes MCP servers, agents, APIs, on‑premises or cloud‑hosted LLMs, and consumer AI tools wired directly to enterprise data.

Shadow AI creates an insider-risk scenario when employees, contractors, or service accounts use AI systems outside approved governance processes—particularly when those systems can access production environments or sensitive data. This activity may be well-intentioned, negligent, or malicious, but in each case it can expose the organization to legal, regulatory, financial, privacy, and cybersecurity harm. Shadow AI is difficult to govern because it may not appear in traditional asset inventories, procurement records, access-review workflows, or approved application catalogs, although evidence of its use may still be visible in endpoint, network, cloud, identity, SaaS, and financial telemetry.

**1\. Living Off the AI (LOTAI)**

Threat actors can reuse a victim’s AI capabilities just as they have “lived off the land” for decades. An attacker who compromises a credential with access to an enterprise AI assistant can summarize and extract sensitive documents without touching the file system in ways DLP or EDR would recognize. An adversary who understands that an internal AI agent has persistent tool‑invocation permissions can manipulate it through prompt injection to perform privileged actions without conventional command‑and‑control traffic.

LOTAI is not a metaphor. It is a logical extension of the adversary decision calculus that produced LOTL. I recently wrote [*Living Off the Land and Living Off the AI*](https://incentivegradient.com/living-off-the-land-and-living-off-the-ai/) on Incentive Gradient, which explores this in more detail.

Although enterprises are rapidly adopting AI, CISOs’ desire to procure new AI‑security tooling is constrained by budget realities and the immaturity of the market. Attack surfaces expand faster than procurement cycles. Some CISOs build internal tools. Others wait for commercial solutions. Some become early adopters of startup products or newly acquired AI‑security offerings.

**There is another option: security teams can use the tools they already have.**

## **Old Wine in New Bottles**

Every meaningful AI threat surface produces signals that existing enterprise controls can already ingest, if configured.

XDR sees network and endpoint behavior across every domain it ingests. SIEM and XSIAM correlate log sources, apply behavioral models, and surface anomalies at scale. IAM governs identity access and enforces least privilege. DLP monitors data movement and content. CASB governs access to cloud services at the boundary. Cloud workload security protects runtime AI environments.

These are operational capabilities. They are operational infrastructure that mature security organizations already manage. The gap is not in tooling. The gap is in the configuration decisions that have not yet been made, the log sources that have not yet been connected, and the behavioral baselines that have not yet been authored for an AI context.

CISOs can achieve meaningful visibility and control over AI systems using the cybersecurity and IT capabilities they already have.

Let’s apply this approach to the two previously identified challenges: Shadow AI and LOTAI.

### **The Two Threat Surfaces of Shadow AI**

Shadow AI involves two different threat surfaces: **shadow AI infrastructure** and **shadow AI usage**. These surfaces require different detection approaches. Conflating them produces a detection architecture misconfigured for both.

![](https://storage.ghost.io/c/72/29/7229993d-4bf0-485a-ae58-2cdc67c51187/content/images/2026/09/Shadow-AI-and-LOTAI.png)

Shadow AI infrastructure and Shadow AI usage are two different detection problems, and CISOs need different parts of their existing security stack to address each one. The remainder of this article provides concise, executive‑level guidance on how each major control including XDR, asset‑discovery platforms, SIEM/XSIAM, IAM, DLP, CASB, vulnerability and exposure management, and cloud workload security contributes to detecting Shadow AI and LOTAI. None of these sections are implementation guides. They are short strategic briefs designed to help CISOs understand where each control fits, what signals matter, and how to configure the stack they already own to achieve meaningful AI visibility.

**1\. Shadow AI Infrastructure: “AI Systems Running Without Security Team Knowledge”**

Shadow AI infrastructure includes AI systems, models, API connections, compute resources, and pipelines deployed without formal IT or security awareness or AI Governance Council (AIGC) approval.

The scope is broader than it appears:

· An employee running Ollama or llama.cpp locally on a corporate laptop.

· A development team spinning up an unauthorized GPU‑enabled VM in a corporate cloud account.

· A business unit wiring a third‑party AI SaaS tool directly to SharePoint without review.

· A production application making direct API calls to an external AI provider using a hard‑coded, unregistered API key.

The security consequence is straightforward: security teams cannot govern, monitor, or respond to AI systems they do not know exist. They cannot apply data‑handling policies to undeclared integrations. They cannot enforce least privilege on service accounts provisioned outside IAM workflows. They cannot detect adversarial use of AI systems that are not in the asset inventory.

Shadow AI infrastructure is the substrate LOTAI inherits — attackers exploit AI systems precisely because no one is watching them.

Detection posture for this surface is primarily a job for XDR: network telemetry, DNS monitoring, endpoint visibility, and cloud workload events surface AI systems that were never formally provisioned. However, XDR can only observe assets where its agent is installed or where it has direct telemetry. That means Shadow AI infrastructure also requires asset‑discovery platforms and tools such as Axonius, JupiterOne, Sevco, and ServiceNow CMDB, to identify unmanaged laptops, undeclared cloud workloads, unauthorized GPU instances, and SaaS AI connections that sit outside XDR’s visibility domain. Together, XDR and asset‑discovery tools provide the structural and behavioral discovery needed to reveal AI systems the organization did not know were running. This surface requires discovery before anything else.

**2\. Shadow AI Usage: “What Sanctioned AI Is Being Used to Do”**

Shadow AI usage is a distinct surface. It refers to anomalous, unauthorized, or adversarial behavior occurring within AI systems that the security team *does* know about; these are systems that were formally procured, approved, deployed, and are governed.

Examples include:

· An attacker using a compromised credential to bulk‑summarize sensitive documents through an enterprise AI assistant.

· A service account with overly broad AI permissions querying data sources outside its intended scope.

· A prompt‑injection payload embedded in a PDF or email ingested by a RAG pipeline, executing adversarial instructions inside a sanctioned AI agent.

The infrastructure is known. What is happening inside it is not being watched.

Detection across this surface often depends on behavioral analytics, supplemented by deterministic rules, policy controls, and configuration monitoring. SIEM/XSIAM, IAM, DLP, CASB, and cloud workload security can provide important detection and enforcement capabilities. Relevant signals may be available in identity, access, application, data, infrastructure, and AI-platform telemetry, although coverage varies by product, deployment model, licensing, and logging configuration. In many organizations, the principal gap is not the complete absence of capable platforms, but limited integration of AI telemetry and immature AI-specific detection logic and behavioral baselines.

💡

****Conflating undeclared AI infrastructure with unauthorized use of sanctioned AI leads to gaps in both discovery and monitoring. Discovery controls help identify AI systems and services the organization has not inventoried; behavioral, identity, data, and runtime controls monitor how known AI systems are used. The two surfaces overlap, but neither control set is sufficient alone.**

The following table summarizes how each control contributes to detecting Shadow AI and LOTAI before we explore them individually.

![](https://storage.ghost.io/c/72/29/7229993d-4bf0-485a-ae58-2cdc67c51187/content/images/2026/09/Control-Table.png)

Each control maps to one of the two detection surfaces — discovery of unknown AI or behavioral monitoring of known AI.

### **Discovery Controls: Finding AI Systems You Did Not Know Were Running**

Shadow AI infrastructure is an asset‑inventory problem disguised as an AI problem. The first question every CISO must answer is simple: *What AI systems are running inside my environment that no one declared?* Answering this requires two complementary forms of discovery. XDR acts as an agent-based and network-based discovery engine, observing active compute patterns, process executions, local model hosting, and outbound API calls in real time. Meanwhile, specialized Asset Discovery platforms (such as Axonius, JupiterOne, Sevco, and ServiceNow CMDB) and Vulnerability & Exposure Management tools provide structural discovery. They scan unmanaged subnets, cloud environments, and configuration inventories to surface rogue GPU instances, unmonitored laptops, and undeclared SaaS integrations that sit outside XDR’s agent coverage. Together, structural visibility and runtime behavioral observation form the baseline required to reveal Shadow AI infrastructure.

XDR is the primary control in the modern enterprise stack capable of answering that question at scale. XDR already ingests the signals required to surface unauthorized AI infrastructure: network telemetry, DNS logs, endpoint behavior, process execution, cloud workload events, and container runtime activity. These signals reveal employees running MCP servers, local LLMs like Ollama or llama.cpp, unauthorized GPU‑enabled VMs serving open‑source models, SaaS AI tools wired directly to enterprise repositories, and production applications making unapproved API calls to external AI providers.

None of this requires new tooling. It requires configuration. XDR can detect AI model downloads, GPU driver activation, localhost model serving, outbound API calls to AI providers, and unusual compute patterns associated with inference workloads. The gap is that most enterprises have not authored detection rules or baselines for AI‑specific behaviors.

Shadow AI infrastructure is the substrate LOTAI inherits. If defenders do not know an AI system exists, they cannot govern it, monitor it, or respond when an adversary uses it against them. XDR is the discovery engine for this surface. Everything else depends on knowing what is actually running.

**SIEM and XSIAM: Behavioral Analytics for AI You Know Is Running**

Once AI systems are formally deployed, the detection problem shifts from *discovery* to *behavior*. Shadow AI usage occurs inside sanctioned systems, which are systems the security team knows exist but has not instrumented for AI‑specific anomalies. This is where SIEM and XSIAM matter.

AI systems produce rich telemetry: audit logs, agent action logs, retrieval events, model‑invocation records, tool‑execution traces, and identity‑linked usage patterns. These signals already flow through SIEM and XSIAM pipelines. What is typically missing is the behavioral analytics layer that interprets them. However, these analytics are entirely dependent on log availability. CISOs must actively use procurement cycles and licensing renewals to demand comprehensive audit telemetry, such as granular retrieval traces, agent tool-invocation logs, and prompt-level metadata, from their SaaS and LLM providers, ensuring this critical signal is not locked behind exorbitant pricing tiers.

An attacker using a compromised credential to bulk‑summarize sensitive documents through an enterprise AI assistant will not trigger traditional DLP or EDR. A service account with overly broad AI permissions querying data sources outside its intended scope will not trigger IAM alerts. A prompt‑injection payload embedded in a PDF ingested by a RAG pipeline will not trigger CASB. But all of these behaviors produce logs SIEM and XSIAM can analyze, if detection rules exist.

Most enterprises have not authored baselines for AI usage: normal query volume, normal data‑source access, normal agent‑tool invocation, normal retrieval patterns. Without baselines, anomalies cannot surface. SIEM and XSIAM are the behavioral engines for sanctioned AI. They reveal *what* AI systems are doing, *who* is doing it, and *whether* those actions align with intended use.

XDR finds unknown AI while SIEM and XSIAM interpret known AI. Both are required.

**IAM: Who Is Actually Talking to Your AI Systems**

AI systems are identity systems. Every model invocation, agent action, retrieval event, and tool execution is performed by a principal, which can be a human user, a service account, or an application identity. IAM is the control that determines who can talk to AI systems and what they are allowed to do.

Shadow AI usage often begins with identity drift: credentials with excessive AI permissions, service accounts with broad model‑access scopes, or application identities that can invoke AI tools far outside their intended function. Attackers exploit these misconfigurations because AI systems frequently inherit identity policies from adjacent services rather than receiving purpose‑built least‑privilege configurations.

As agentic architectures proliferate, this identity boundary extends beyond human users. Autonomous AI agents, MCP server integrations, and automated pipelines interact with systems using delegated Non-Human Identities (NHIs); these include service principals, API keys, and OAuth tokens. Governing AI through IAM requires monitoring not just who invoked a model, but what delegated identity the agent assumed, what down-scoped permissions were granted during tool execution, and whether token exchanges remain tightly bounded.

IAM already provides the signals required to detect identity misuse such as anomalous access patterns, privilege escalation, cross‑domain data access, and identity‑linked AI usage spikes. The gap is that most enterprises have not mapped AI systems into their identity governance workflows. AI assistants, RAG pipelines, and internal agents often sit outside formal access‑review cycles.

LOTAI is fundamentally an identity problem. Attackers use valid credentials to perform invalid actions through AI systems. IAM is the control that reveals who is interacting with AI, whether those interactions align with intended roles, and where privilege boundaries have eroded.

Identity is the lens through which AI usage becomes visible.

**DLP: The Data Flowing into and Out of Your AI Systems**

AI systems are data engines. They ingest, transform, summarize, classify, and generate content. DLP is the control that monitors what data flows into AI systems and what data flows out, and whether those flows violate policy.

Shadow AI usage frequently manifests as data misuse such as employees pasting sensitive documents into external AI tools, service accounts retrieving data far outside their functional scope, or attackers using compromised credentials to extract sensitive content through sanctioned AI assistants. Traditional DLP does not detect these behaviors because the data never moves across the file system or a network in ways DLP was designed to observe.

However, modern DLP platforms already ingest content‑inspection signals, SaaS usage telemetry, and API‑level data‑flow events. AI systems produce logs that reveal what data was retrieved, summarized, or transformed. The gap is that enterprises have not integrated AI audit logs into DLP workflows or authored policies that treat AI interactions as data‑movement events.

DLP can detect sensitive data flowing into AI systems, sensitive data flowing out of them, and anomalous patterns of content access. It can enforce policy at the boundary between data stores and AI systems. But only if AI telemetry is connected and AI‑specific policies exist.

AI systems are new data‑movement surfaces and DLP is the control that governs them.

**CASB: Governing Cloud AI at the Service Boundary**

Most AI usage in enterprises occurs in SaaS environments such as cloud‑hosted LLMs, AI‑augmented productivity suites, embedded AI features in collaboration tools, and external AI APIs. CASB is the control that governs access to these services at the boundary.

Shadow AI infrastructure often begins with unsanctioned SaaS AI tools wired directly to enterprise data. Shadow AI usage often occurs inside sanctioned SaaS platforms where AI features are enabled by default. CASB already provides visibility into SaaS usage, data access, identity behavior, and API interactions. It can reveal which AI services employees are using, what data they are sending, and whether those services comply with enterprise policy.

The gap is that most enterprises have not updated CASB policies to treat AI services as high‑risk SaaS. AI features inside productivity suites often bypass CASB entirely because they are considered part of the core application rather than distinct services. CASB can enforce governance at the boundary, but only if AI services are classified, monitored, and controlled.

AI is becoming a SaaS boundary problem and CASB is the control that governs that boundary.

**Cloud Workload Security: Runtime Protection for AI Infrastructure**

AI infrastructure increasingly runs in cloud environments: GPU‑enabled VMs, containerized inference services, vector databases, RAG pipelines, and agent‑execution environments. Cloud workload security is the control that protects these systems at runtime.

Shadow AI infrastructure frequently appears as unauthorized cloud workloads: unapproved GPU instances, containerized model servers, or pipelines deployed outside sanctioned environments. Cloud workload security already monitors runtime behavior, process execution, network flows, container activity, and identity interactions. These signals reveal AI workloads that were never declared.

Shadow AI usage also manifests at runtime: prompt‑injection payloads executed inside containers, agent‑tool invocations that perform privileged actions, or inference workloads that access data stores outside intended scope. Cloud workload security can detect these behaviors because they produce runtime anomalies.

The gap is that most enterprises have not mapped AI workloads into their cloud‑security posture. AI systems often run in development environments, shadow cloud accounts, or experimental pipelines that bypass runtime controls.

AI infrastructure is a runtime problem and cloud workload security is the control that governs runtime.

**Vulnerability & Exposure Management: Discovering AI Infrastructure Through Asset Visibility**

Vulnerability and Exposure Management can help identify IT assets where AI components operate. These platforms inventory servers, containers, and cloud resources to reveal where MCP servers, local inference agents, and model‑hosting environments are running, even when they were never declared to the security team. By correlating configuration data, network exposure, and dependency mappings, they can surface unmanaged AI workloads and misconfigured systems. Shadow AI infrastructure often hides inside development networks or cloud accounts registered under non‑security ownership; exposure‑management tools identify these assets, classify their risk, and connect them to governance workflows. This control transforms vulnerability scanning into structural discovery, enabling CISOs to locate and assess the physical and virtual hosts that form the substrate of Shadow AI and LOTAI activity.

**The Detection Coverage Map**

Shadow AI and LOTAI are two distinct detection problems:

· **Shadow AI infrastructure:** AI systems running without security team knowledge

· **Shadow AI usage**: unauthorized behavior inside the AI systems the security team knows about

![](https://storage.ghost.io/c/72/29/7229993d-4bf0-485a-ae58-2cdc67c51187/content/images/2026/09/Control-Coverage-Map.png)

****Discovery → Behavior → Runtime Protection Interlock** \- **each layer feeds the next to form continuous AI visibility.*

The detection coverage map is simple: **XDR finds what you don’t know is running. Vulnerability and Exposure Management reveals where it’s running. Your behavioral analytics stack watches what you do know is running. They are not interchangeable.**

This is the architecture CISOs can deploy today, using the tools they already have.

## **What This Means for AI‑Security Innovation**

VCs and founders evaluating the AI‑security landscape should anchor their innovation strategy to these two detection surfaces. Solutions that merely stitch together signals from existing cybersecurity tools into new dashboards will not deliver enduring value for CISOs; they do not solve the discovery problem for Shadow AI infrastructure, nor the behavioral‑monitoring problem for sanctioned AI usage. **It is also easy for incumbents to replicate or subsume lightweight AI‑security features, which means products that aggregate signals or provide new dashboards will be quickly replaced or rendered irrelevant.** The opportunity lies in extending the capabilities enterprises already operate — enriching XDR, SIEM/XSIAM, IAM, DLP, CASB, vulnerability management, and cloud workload security with AI‑specific detection logic, telemetry, and governance controls. The long‑term winners will be the products that strengthen the existing stack, not those that attempt to replace it.