This whitepaper outlines the capabilities of CloudSphere Shadow AI Discovery – the unauthorized or unmanaged use of AI components within an enterprise and its relationship to identifying and assessing risks within an organization’s IT infrastructure.
The Growing Threat of Shadow AI
As users rapidly embrace the utility of AI at scale, unauthorized instances of shadow AI have proliferated across enterprise IT networks. These unmanaged AI instances create a growing security concern for CISOs who lack visibility into where AI models are being deployed, what data is being accessed and processed, and what threats they pose to corporate data and IP security. This visibility gap makes it difficult to enforce consistent security policies and undermines data governance and regulatory compliance efforts.
Risks Associated with Shadow AI in IT Infrastructure
Management at all levels of organizations, from Boards of Directors and CEOs to functional managers, are pushing enterprise employees to incorporate AI into their daily work in order to gain productivity, become more efficient, and gain new insights from enterprise data. As a result of this rapid deployment and use of AI, concerns associated with Shadow AI may create new potential impacts on a number of business risk domains.
Data Privacy and Security Vulnerabilities
- AI Models are highly dependent on their data sources during the creation of the models or when the models are deployed. Risks may include the exposure of sensitive data or enterprise IP to unauthorized parties or weak security protocols. Moreover lack of encryption and secure APIs in unapproved AI tools could lead to the potential for data exfiltration and data loss.
Exploding Cloud Costs
As use of AI agents autonomously executing actions across the network increases exponentially, the potential for thousands of agents each rapidly deploying new cloud instances becomes a serious cost management issue. Likewise, low or no code tools that allow business users to deploy code and execute actions in the network holds the potential for thousands of cloud services to rapidly be initiated, spiking cloud costs.
Change Management
As noted above, autonomous agents and many new “citizen developers” enabled by low code tools can rapidly execute changes to cloud services, network configurations and infrastructure. Tracking and managing these changes quickly overwhelm IT staff and the tools they currently rely on. Traditional CMDBs as an inventory tool are often considered unreliable in today’s pre-agentic IT management world, as they require laborious manual updating, and maintaining them is typically not a top developer priority. In a world of rapid changes driven by agents and no code assistants, maintaining an accurate inventory and tracking changes over time requires modern tools that provide comprehensive visibility into all components in the IT estate.
Non-Compliance with Regulations:
In the realm of AI regulations, compliance, security, and audits are all intertwined. Ensuring compliance requires comprehensive visibility into all elements across the IT estate. Without AI risk visibility & monitoring, violations of data protection regulations (e.g., GDPR, HIPAA, CCPA, DORA, etc.) may result in potential legal action and fines. New regulations and frameworks are emerging globally, like the EU AI Act and discussions around an Artificial Intelligence Liability Directive.
Operational Inefficiencies and Data Management Challenges:
- Authorized and un-authorized data silos may be created by disparate AI tools. In addition, use of unauthorized data sources accessed by shadow AI could produce bad results, hurting productivity or even detrimental results. As one example, if a shadow AI deployed by marketing accessed out of date customer information in a CRM system, it might trigger an inappropriately targeted email campaign, while collecting responses that need to be merged back to the authorized CRM. This would increase the difficulties of maintaining data accuracy and integration and increase operational costs for duplicative activities.
Cybersecurity Threats:
- Introduction of Shadow AI tools increases the IT attack surfaces and subsequently potential entry points for cyberattacks. The
- Vulnerabilities are system-wide, cutting across AI Models, AI Data sources, Agents, Cloud Services, Applications and other AI Consumers.
Who Should Care About Shadow AI?
Given the broad range of business risk domains impacted by shadow AI, responsibility for discovering shadow AI is most likely shared among a number of constituents across the enterprise including CISOs, CIOs, AI strategy & Ops teams, and corporate risk officers. Having one tool that can provide a unified, comprehensive view across the entire IT estate is critical to enable coordinated, timely remediation. Ongoing monitoring of the network is essential to identify new instances of shadow AI as they spring up, as well as to monitor remediation efforts of identified instances via red teaming and testing as well as policy adjustments.
CloudSphere Shadow AI Discovery Platform
CloudSphere’s Illuminate360 platform is engineered to perform comprehensive, agentless scanning across the entire IT estate – multi-cloud, hybrid, and on-premises environments. It discovers and identifies servers, applications, and cloud services, mapping relationships and interdependencies to generate detailed service maps. By discovering and identifying shadow AI applications, as well as the network topology surrounding them, CloudSphere provides comprehensive IT visibility that empowers security teams to regain control and mitigate the risks posed by these unmanaged AI instances.
Shadow AI Discovery on Prem
CloudSphere’s illuminate360 uses agentless scanning to discover instances of unauthorized shadow AI running on servers and connected endpoint laptops & computers (excluding IoT endpoints) that have been deployed in on-premises environments. Illuminate360 also identifies the operational data sources and other applications connected to the shadow AI instances.
Shadow AI Discovery in the Cloud
Using standard cloud management console credentials, Illuminate360 discovers managed services in use – databases, GenAI models , serverless compute, serverless orchestration, messaging queues & notifications, etc.
Network Context and Definitive Knowledge Graph
After the major components are catalogued, application communications traffic is then analyzed to build up a comprehensive map of resources, services, and their relationships specific to each customer environment. These service maps visualize the network context in which shadow AI instances are deployed, highlighting data sources that can be accessed and applications that are connected to the model, allowing users to visualize the potential risks caused by the AI applications. Armed with this contextual information, IT managers can identify sensitive data sources that must be secured and prioritize remediation efforts based on the criticality of business services impacted. For example, shadow AI models deployed in an ecommerce service should be addressed immediately, while those in QA environments can be addressed on a less urgent basis.
This scanned data is then compiled into a definitive knowledge graph, which becomes the single source of truth inventory of all IT assets in the network. CloudSphere’s knowledge graph can be used to update or replace the CMDB as well as to define network elements that may be acted upon by agents.
Conclusion
Leveraging the power of AI has become an imperative for enterprises today, and many employees are taking the initiative by experimenting with AI applications. IT managers must strike a balance between enabling the full productivity and efficiency gains of these tools while ensuring that AI is used safely and responsibly. In order to achieve that balance, CIOs and CISOs must have adequate visibility across their IT networks to be able to answer three key questions:
- What AI Models are in use?
- What is the network and business context for each model (where are they deployed in the network)?
- What data source(s) or other applications are accessed by the model?
By addressing these questions, the risks posed by shadow AI can be identified, prioritized, and remediated, without hindering the productivity gains available from safe, responsible AI deployment.
AI audit, compliance, or security management activities all begin with the identification and assessment of risks across all IT elements (AI models, agents, applications, functions, data sources). The identified risks are then prioritized by the context of the business services in which they are employed. Furthermore, risk exposure can be seen by the neighboring elements that are connected to them by way of service maps.
Take Control of Your AI Landscape Today
Don’t let shadow AI put your organization at risk. CloudSphere’s Illuminate360 platform empowers you to discover, assess, and manage all AI instances across your entire IT estate – giving you the visibility and control you need to harness AI’s potential while protecting your data and infrastructure. Whether you’re a CISO concerned about security vulnerabilities, a CIO managing cloud costs, or a risk officer ensuring compliance, CloudSphere provides the comprehensive insights you need to make informed decisions.
Ready to illuminate your shadow AI landscape? Contact CloudSphere today to schedule a demo and see how Illuminate360 can help you achieve safe, responsible AI deployment across your enterprise.