How Embedded AI is Quietly Expanding Your Attack Surface

What Is Embedded AI, and Why Is It Invisible by Design?

Embedded AI refers to AI-powered features built directly into widely used SaaS platforms. These include tools like Microsoft 365 Copilot, Grammarly, Slack AI, Zoom AI Companion, Notion AI, and many others. Unlike standalone AI tools, embedded AI features are accessed within familiar workflows and software.

They do not require separate authentication, appear as native functionalities, and are often enabled by default. There is no separate domain to monitor, no external traffic to flag, and no formal rollout to control.

Consider these common examples:

• Microsoft 365 Copilot generates content using SharePoint data, including sensitive documents, when used in unmanaged or personal accounts, bypassing enterprise policies and permission boundaries.
• Slack’s AI features summarize entire threads, extracting and storing conversational data that may contain regulated or proprietary information.
• Zoom AI Companion generates meeting summaries, action items, and highlights, drawing from real-time discussions that often contain personal, strategic, or compliance-sensitive content.

Why Traditional Security and Governance Architectures Fall Short

Conventional security frameworks are designed to detect familiar threat vectors: unauthorized applications, external data transfers, and anomalous user behavior. Embedded AI bypasses these controls entirely.

• Blocklists are ineffective when AI is integrated into approved platforms.
  For example, Microsoft 365 Copilot operates within a trusted suite of tools. Even if access to external tools like ChatGPT is blocked, users can still generate sensitive content from SharePoint without raising any alerts.
  
  
• Allowlists lack the granularity to differentiate between corporate and personal account activity.
  A user can log in to Notion AI using a personal email address, upload confidential work documents, and interact with embedded AI features. Since Notion is allow-listed, these actions proceed without policy enforcement or visibility.
  
  
• Static policies do not account for user role or business context.
  A summarization feature might be acceptable in a marketing workflow, but in legal or finance, it could inadvertently process client contracts or regulatory documents. Traditional DLP tools cannot adjust risk posture based on organizational function or data type.
  
  

• Blanket restrictions reduce functionality but fail to eliminate exposure.
  Disabling AI summarization in Slack may frustrate users without mitigating risk. The same individuals might continue using Zoom AI Companion or Notion AI for similar tasks, which would be entirely outside the scope of existing controls.

Fundamentally, today’s governance frameworks are not built to interrogate what AI models embedded in enterprise platforms are doing. Without insight into the data they access, the outputs they generate, or the decisions they influence, organizations lack the situational awareness required to govern these systems effectively.

The Unique Risk Profile of Embedded AI

The risk surface introduced by embedded AI is multifaceted and expanding across several critical dimensions:

• Unauthorized Model Training: Embedded AI features may draw on enterprise data to fine-tune or retrain models without explicit organizational consent. Internal documents, private communications, and customer records can inadvertently become part of the model’s learning substrate.
  
  
• Excessive and Inherited Privileges: These models typically operate with the full set of permissions granted to the host application, allowing for unrestricted access. This includes access to file systems, message archives, and CRM data. The scope of access is broad, but the activity is rarely logged or auditable at a granular level.
  
  
• Regulatory Compliance Risk: Embedded AI may violate jurisdictional compliance standards, such as the GDPR, HIPAA, or the EU AI Act, by processing or storing sensitive data without obtaining enforceable consent, failing to implement data minimization principles, or establish adequate retention controls.
  
  
• Third-Party Data Processor Exposure: When embedded AI features are powered by external providers, such as OpenAI or Anthropic, those vendors often act as data processors—sometimes retaining logs or processing sensitive content outside the enterprise’s direct control. This creates an indirect risk channel that is difficult to audit and frequently overlooked in vendor governance workflows.

This risk is not diminishing. It is accelerating.

Gartner projects that by 2026, 40% of enterprise applications will include task-specific AI agents, and 80% of independent software vendors will embed AI into their platforms.

At the same time, Gartner’s 2025 TRiSM guidance identifies embedded and agentic AI as priority governance targets, citing a growing gap between policy and practice across enterprises. 

Most organizations have documented AI policies, but they lack the runtime enforcement and contextual controls needed to implement them effectively. With legal mandates expected by 2027, the requirement to govern embedded AI is no longer optional.

Making Embedded AI Visible with Singulr

Singulr is the first Unified AI Control Plane architected to detect and govern all forms of AI across the enterprise landscape, including embedded AI, public generative AI services, and internally developed systems. Its layered architecture and contextual intelligence deliver the operational clarity, security, and governance leaders require to manage AI risk at scale.

Core capabilities

AI Discovery with Context

Singulr continuously inventories AI activity across the enterprise stack. This includes not only sanctioned applications and proprietary models, but also embedded AI features that operate within trusted SaaS platforms.

The system identifies specific AI capabilities in use, such as summarization, chat, coaching, or drafts by Copilot, along with the user and the usage context for supported platforms like Microsoft 365.

Singulr Pulse: AI Risk Intelligence Layer

This continuously updated knowledge graph catalogs millions of AI agents, services, datasets, and models, enriched with metadata on:

• Model training and data retention policies
• Regulatory certifications (e.g., GDPR, HIPAA, SOC 2)
• Risk profiles and trust indicators
• Hosting jurisdictions and provider lineage

This intelligence enables real-time evaluation of model behavior, risk posture, and compliance alignment.

Identity-Correlated Insights

Singulr maps AI usage to individual user identities and privilege levels. It distinguishes between corporate and personal account activity, flags cross-domain usage, and highlights interactions involving regulated business units. This is essential for detecting unauthorized access and preventing exposure through shadow AI.

AI Transparency

Singulr addresses the “black box” problem every large enterprise faces. Long-standing software and SaaS relationships are being transformed by solution providers incorporating AI agents, services, and features that you can’t see and that they may not be disclosing. Traditional security and governance architectures fall short when faced with this problem - you approved the use of these solutions without factoring in AI use cases. Singulr allows you to see who and how those solutions are leveraging AI.

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Enhanced Runtime Controls

Singulr enforces live, context-aware controls designed for high-risk environments, including:

• Redaction of sensitive data from prompts or outputs
• Prevention of file uploads or model execution under defined risk conditions
• User guidance toward compliant alternatives
• Integration with incident response workflows for elevated threats
  
  

Executive-Level Dashboards and Compliance Reporting

Singulr provides centralized visibility through real-time dashboards, enabling stakeholders to monitor AI activity across departments, platforms, and risk categories.

Reporting is designed to support alignment with major regulatory and industry frameworks, including the EU AI Act, NIST AI Risk Management Framework, and ISO/IEC 42001. All relevant AI interactions are logged, categorized, and linked to the organization’s broader compliance and risk posture.

Key Takeaways for Enterprise Security and Governance Leaders

• Embedded AI is now widespread across enterprise SaaS platforms. It is often enabled by default, inherits broad permissions, and operates without clear visibility.
• Traditional security and governance frameworks are not equipped to detect or control AI functionality embedded within approved applications.
• The risks include unmonitored data access, regulatory non-compliance, and unauthorized model training on sensitive or proprietary data.
• Singulr delivers a purpose-built control plane to govern embedded AI. It provides real-time discovery, contextual risk scoring, and runtime policy enforcement.
• To align with emerging regulatory mandates and uphold enterprise trust, organizations must surface and govern embedded AI as a first-class risk vector.
  
  

Embedded AI is not a future concern. It is already operating within your most trusted applications, often without oversight. Singulr helps security and governance teams surface these blind spots, assess real risk, and enforce policies at scale without slowing innovation.

To see how Singulr can bring visibility and control to embedded AI in your environment, request a personalized demo with our team.

Book a Demo Here.

Frequently Asked Questions

1. What qualifies as embedded AI within an application?

Embedded AI refers to AI-powered capabilities that are integrated directly into widely used SaaS platforms. These features are native and don’t require separate user action, authentication, or deployment. Common examples include document summarization in Microsoft 365, conversation analysis in Slack, and predictive insights in Salesforce or Notion.

2. Why is embedded AI difficult to detect using traditional security tools?

Embedded AI runs within approved software, inheriting the platform’s permissions, and generates activity that resembles standard user behavior. They do not create new domains, require separate authentication, or trigger conventional anomaly detection, making them effectively invisible to legacy controls.

3. What are the compliance risks associated with embedded AI?

Embedded AI features process, store, or train on data that includes personally identifiable information (PII), protected health information (PHI), and other regulated content. Without oversight and controls, they can lead to violations under frameworks such as GDPR, HIPAA, and the EU AI Act.

4. Can enterprises disable embedded AI features?

In most enterprise-tier plans, vendors offer administrative controls to manage embedded AI functionalities. These include the ability to disable data sharing for model training and configure data retention policies to restrict data exfiltration. However, these controls are often unavailable or limited in personal, free-tier, or unmanaged accounts. Even where controls exist, they may require manual configuration and lack the granularity needed for continuous, context-aware enforcement across diverse user groups.

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