SaaS applications now account for one of the most actively targeted layers of the enterprise attack surface, with 75% of organizations reporting a SaaS-related security incident in the past year¹. Despite this, many teams remain overconfident in their visibility and control. For security vendors, this gap has elevated SaaS Risk Management from an enterprise challenge into a defining opportunity.
The challenge, however, is no longer limited to sanctioned SaaS applications. The rapid adoption of AI Assistants (also referred to as AI copilots) and LLM-powered tools has introduced a new layer of risk. These applications can generate content, access sensitive data, and connect with other SaaS platforms autonomously, which makes them far harder to monitor and control with traditional approaches. They are being adopted at a faster pace than most security teams can track, increasing the likelihood that AI apps are already active within customer networks.
Knowing that a customer uses Salesforce, Slack, or a new AI assistant may satisfy basic visibility requirements, but it does not equip vendors to help their customers manage risk effectively. What is missing is context: intelligence about what each app does, how it connects, and what risks it introduces.
Q: What role does Enriched SaaS Intelligence play in extending SaaS and AI Risk Management for vendors?
Enriched intelligence transforms raw discovery into actionable data. It allows SaaS security platforms to classify applications and their functions, identify AI assistants and agents, apply risk scoring, and deliver stronger security outcomes for their customers.
The Visibility Data Blind Spot in SaaS and AI Risk Management
Discovery is the foundation of SaaS Risk Management, but it is also where many platforms stop. A raw list of applications may show that dozens or even hundreds of SaaS tools are in use, but it does not explain what those applications do, which functions they support, or what level of risk they present. Without enrichment, discovery alone leaves both vendors and their customers exposed.
This blind spot is even more pronounced with AI assistants and LLM applications. These tools often operate outside the scope of traditional monitoring. An assistant may act as a non-human identity with access to sensitive data. Because they can be installed and activated quickly, many AI apps are already running inside enterprise networks without vendor or customer awareness. Certain app functions, such as file sharing or automated transcription, can introduce exposure risks that remain invisible without classification and risk scoring. Without enriched SaaS intelligence to categorize these applications and evaluate their risk, platforms cannot surface the full scope of exposure or support effective AI risk management.
Q: Where do SaaS Risk Management tools fall short without enrichment?
They lack the contextual intelligence that turns discovery into actionable data. Without categories, risk ratings, and coverage of AI apps, vendors cannot deliver accurate risk scoring or meaningful prioritization.
Q: Why are AI Assistants and LLM applications especially challenging for vendors?
Because they introduce risks that are both dynamic and opaque. Assitants can interact with multiple systems autonomously, LLMs can generate sensitive outputs based on internal data, and SaaS apps with risky functions can move information beyond the reach of traditional controls. Without enriched SaaS intelligence, these activities remain invisible.
(For a deeper look at AI-specific risks, see our earlier article on AI Agent Risk Mitigation. This post focuses on how vendors can close these gaps by embedding enriched SaaS intelligence into their platforms.)
Why Vendors Need Enriched SaaS Intelligence
For SaaS security vendors, discovery is only the starting point. The real measure of value is whether a platform can help customers manage the risks those applications introduce. That requires going beyond raw discovery to provide enrichment: categorization, context, and risk intelligence that make the data actionable within the platform.
Discovery can tell a vendor that a customer is using Salesforce, Slack, or a new AI assistant. Enrichment tells the vendor what type of application it is, which functions it supports (such as file sharing, messaging, or transcription), and whether it presents elevated risk based on its category or behavior. Without enriched SaaS intelligence to categorize these applications and evaluate their risk, platforms cannot surface the full scope of exposure or support effective AI Risk Management alongside SaaS coverage.
The rise of AI assistants and LLM applications has made this need even more urgent. These tools often act as non-human identities, interact autonomously across SaaS environments, and handle sensitive data. Without enrichment to detect and classify them, platforms cannot deliver accurate risk scoring, surface priority issues, or provide meaningful context for remediation.
Q: How does Enriched SaaS Intelligence enable vendors to deliver stronger SaaS and AI Risk Management?
It gives platforms the structured, contextual data they need to extend their existing capabilities. For example:
- SaaS Security Posture Management (SSPM) platforms can strengthen visibility by classifying applications by type and function, then applying risk scoring to highlight those with higher exposure potential.
- Data Security Posture Management (DSPM) platforms can identify which SaaS and AI applications are likely to process or generate sensitive data, improving governance and compliance monitoring.
- Identity Threat Detection and Response (ITDR) platforms can identify AI assistants as non-human identities, helping to detect abnormal usage patterns or access risks.
Q: What risks do vendors leave unaddressed without AI Application Risk Management capabilities?
They leave blind spots where today’s fastest-growing risks exist. AI assistants, LLM applications, and SaaS apps with high-risk functions can operate unseen, creating exposures that weaken trust in a platform. By embedding Enriched SaaS Intelligence, vendors ensure their platforms evolve in step with the expanding SaaS and AI landscape.
SaaS App Intelligence as the Enrichment Layer
Solving the SaaS and AI Risk Management challenge requires more than discovery. Security vendors need enrichment that categorizes applications, classifies their functions, applies risk scoring, and delivers coverage that spans both traditional SaaS apps and emerging AI tools. That is the role of SaaS App Intelligence.
Unlike raw discovery, which simply identifies the presence of applications, SaaS App Intelligence delivers:
- Categorization — defining what an application is (for example, CRM, collaboration, file sharing, or AI assistant).
- Function classification — identifying the functions an application or endpoint supports, such as upload, download, messaging, or transcription.
- Risk scoring — evaluating exposure potential based on app type, function, and sensitivity to data handling.
- Coverage breadth — extending beyond sanctioned SaaS apps to shadow SaaS and AI assistants/LLMs.
- API-first integration — enabling vendors to easily embed enrichment into their platforms without heavy engineering effort.
Q: What distinguishes Enriched SaaS Intelligence from raw discovery for vendors?
Discovery can confirm that an application exists. Enriched intelligence explains what kind of application it is, what functions it enables, and whether it presents risk. This additional context allows vendors to make their platforms more effective at governance, remediation, and monitoring.
Q: How can vendors embed SaaS App Intelligence into their SaaS and AI Risk Management workflows?
Through API-first delivery. SaaS App Intelligence provides structured, normalized data that can be integrated directly into SSPM, DSPM, or ITDR workflows. This enables vendors to apply enrichment across their existing features, accelerating development and delivering immediate value to customers.
For vendors, SaaS App Intelligence is not a competing product — it is an enabling layer. By embedding enriched SaaS intelligence, they can extend their platforms beyond discovery and address the risks that matter most, from shadow SaaS to AI assistants and LLM applications.
Vendor Use Cases: Extending Platform Value with Enrichment
Security vendors across different segments approach SaaS Risk Management from unique angles — posture, data, identity — but all face the same challenge: raw discovery alone lacks the context needed to assess risk. By embedding Enriched SaaS Intelligence, each category of platform can extend its value and deliver stronger outcomes to customers.
Q: How can SaaS Security Posture Management (SSPM) platforms improve with enrichment?
SSPM platforms already focus on configuration checks and policy enforcement across sanctioned SaaS. With Enriched SaaS Intelligence, they can classify applications by type, identify which ones support higher-risk functions such as file sharing or automated messaging, and apply risk scoring to help customers prioritize remediation.
Q: How can Data Security Posture Management (DSPM) platforms extend governance to SaaS and AI applications?
DSPM platforms focus on identifying and protecting sensitive data. By embedding enrichment, they can determine which applications are most likely to handle or generate sensitive data, including AI assistants and LLM apps. Without enriched SaaS intelligence to categorize these applications and evaluate their risk, platforms cannot surface the full scope of exposure or support effective AI Risk Management alongside SaaS coverage.
Q: How can Identity Threat Detection and Response (ITDR) platforms strengthen monitoring for AI-driven risks?
ITDR platforms monitor for identity compromise. Enriched SaaS Intelligence helps them identify non-human identities tied to AI assistants, classify those applications, and highlight unusual patterns of access or behavior. This allows ITDR platforms to extend their monitoring into the growing category of AI-driven applications.
These use cases illustrate how different vendor categories can use Enriched SaaS Intelligence as an enabling layer. By embedding categorization, function classification, risk scoring, and AI app detection, vendors can evolve their platforms to meet the realities of SaaS and AI Risk Management.
Best Practices for Vendors Integrating Enriched SaaS Intelligence
For security vendors, adding Enriched SaaS Intelligence is not simply about filling a visibility gap. It is about creating a foundation that enhances existing features, accelerates development, and delivers greater value to customers. To maximize the impact, vendors should consider several best practices when embedding enrichment into their SaaS and AI Risk Management workflows.
Q: What should vendors prioritize when selecting an enrichment partner?
The key is balancing breadth and depth. Vendors should look for coverage that spans sanctioned SaaS, shadow SaaS, and AI assistants, combined with detailed categorization, function-level classification, and contextual risk scoring.
Q: How does Enriched SaaS Intelligence accelerate vendor roadmaps?
By being delivered through API-first integration. Structured and normalized data that can be consumed via APIs or SDKs reduces engineering overhead, allowing vendors to embed new capabilities without building the enrichment layer themselves.
Q: How can vendors ensure continuous value as the SaaS and AI ecosystem evolves?
They should work with partners committed to continuous updates. SaaS and AI applications evolve rapidly, and enrichment must evolve with them to maintain accurate classifications, coverage of new apps, and updated risk scoring.
Checklist for Vendors
- Coverage of SaaS apps, shadow SaaS, and AI assistants/LLMs
- Rich categorization and function classification
- Contextual risk scoring
- API-first integration for ease of adoption
- Continuous intelligence updates to match the pace of SaaS and AI evolution
For vendors, the takeaway is clear: embedding Enriched SaaS Intelligence is less about replacing existing capabilities and more about amplifying platform value. It enables more accurate SaaS and AI Risk Management, faster remediation, and stronger differentiation in a crowded market.
The Vendor Opportunity in SaaS and AI Risk Management
SaaS Risk Management has become one of the defining challenges for security vendors. Enterprises now expect their platforms to do more than identify SaaS applications. They expect them to classify applications, evaluate risks, and account for the growing adoption of AI assistants and LLM tools that operate autonomously across their environments.
Enriched SaaS Intelligence provides the enabling layer vendors need to meet this expectation. By embedding categorization, function-level classification, risk scoring, and coverage for shadow SaaS and AI applications, vendors can extend their existing platforms and deliver stronger outcomes in both SaaS and AI Risk Management.
Success will favor vendors who evolve their platforms with enriched SaaS App Intelligence. Those who embed enrichment will not only close today’s visibility gaps but also position themselves to address the next wave of SaaS and AI risks with confidence.
Have questions about how enriched SaaS Intelligence can strengthen your platform? Contact us today and start the conversation.
¹ ITPro: SaaS security is becoming a major blind spot for enterprises
