Webinar Description
Key Takeaways
- Shadow AI refers to generative AI tools, coding assistants, browser extensions and custom plugins deployed without formal IT approval
- The webinar addresses detection and monitoring of unauthorised AI usage across enterprise environments
- Topics include Model Context Protocol activity monitoring, AI plugin detection and policy framework development
- Designed for CISOs, security operations teams, IT leadership and compliance professionals
- Particularly relevant for organisations in regulated industries with significant SaaS adoption
Introduction
AgileBlue and Intelisys are hosting a webinar on 23 July 2026 examining the security challenges posed by Shadow AI—the proliferation of artificial intelligence tools within organisations that operate outside formal IT governance. The session targets security professionals, IT leadership and compliance teams grappling with visibility gaps created when employees adopt generative AI assistants, coding tools and browser extensions without centralised oversight. As AI capabilities become embedded in everyday productivity software, the attack surface available to threat actors has expanded in ways that conventional security monitoring was never designed to address.
About This Event
The sixty-minute virtual session promises a technically focused examination of Shadow AI risks, moving beyond general awareness into practical detection and control strategies. AgileBlue, a cybersecurity vendor specialising in threat monitoring, leads the presentation alongside Intelisys, a channel-focused technology partner. The format combines technical presentations with real-world breach scenarios, a partner spotlight segment and live audience questions.
The organisers position the event as a practical alternative to the marketing-heavy discussions that often surround AI security topics. Rather than speculating about future threats, the session focuses on risks that security teams are encountering today and the monitoring approaches required to address them.
Understanding Shadow AI as an Attack Surface
Shadow AI represents a natural evolution of the shadow IT phenomenon that has challenged security teams for over a decade. Where previous generations of unsanctioned technology adoption involved cloud storage services or collaboration platforms, the current wave centres on AI-powered tools that can process, analyse and transmit sensitive data in ways that are difficult to observe through traditional network monitoring.
The category encompasses several distinct technology types. Generative AI assistants integrated into productivity applications may send document contents to external inference endpoints. Coding assistants can expose proprietary source code to third-party training pipelines. Browser extensions with AI capabilities often operate with broad permissions that allow them to read page contents across all visited sites. Custom plugins and integrations built on large language model APIs may lack the security controls applied to formally procured enterprise software.
Each of these tool categories creates potential data exfiltration pathways that exist outside the visibility of security information and event management systems configured for conventional threat patterns. The webinar addresses how these blind spots emerge and what detection approaches can illuminate them.
Detection and Monitoring Approaches
A significant portion of the session focuses on the technical mechanisms required to identify Shadow AI activity within enterprise environments. The organisers highlight several specific detection domains that the presentation will address.
Model Context Protocol monitoring represents an emerging area of security focus. As AI applications increasingly communicate with external models through standardised protocols, security teams require visibility into these interactions to understand what data is being shared and with which endpoints. The webinar will examine how MCP activity can be observed and what anomalous patterns might indicate.
Detection of unauthorised AI coding tools presents particular challenges in development environments where legitimate AI assistance may be permitted for some use cases but prohibited for others. The session addresses how organisations can distinguish between sanctioned and unsanctioned usage when the underlying technologies may be similar.
Browser and file extension risks receive dedicated attention, reflecting the reality that many AI capabilities reach end users through extension marketplaces rather than formal software deployment channels. These extensions often request permissions that would trigger security reviews if requested by traditional enterprise applications but pass unnoticed when installed by individual users.
Breach Statistics and Business Impact
The webinar incorporates breach data to contextualise the business risks associated with Shadow AI. The organisers cite IBM research indicating that one in five organisations with significant SaaS adoption experienced breaches involving Shadow AI during 2025. The same data suggests that Shadow AI involvement adds approximately $670,000 to the average cost of a security incident.
These figures reflect both the direct costs of incident response and the complications that arise when security teams must investigate breaches involving tools they did not know existed within their environment. Forensic analysis becomes substantially more difficult when the data flows under investigation were never logged, and regulatory reporting obligations may be complicated by uncertainty about exactly what information was exposed.
Policy Frameworks and Control Selection
Beyond detection technology, the session addresses the governance frameworks required to manage Shadow AI risk systematically. Technical controls alone cannot address a challenge that fundamentally involves human behaviour and organisational culture. Employees adopt unsanctioned AI tools because those tools help them work more effectively, and security policies that simply prohibit all AI usage are unlikely to achieve compliance.
Effective Shadow AI governance typically requires a combination of technical controls that provide visibility, policies that distinguish between acceptable and unacceptable AI usage, and approved alternatives that give employees legitimate pathways to access AI capabilities. The webinar will examine how organisations can develop control frameworks that balance security requirements against operational needs.
Who Should Attend
The session is designed for security and IT professionals responsible for protecting enterprise environments against emerging threats. Chief Information Security Officers and security operations teams will find the detection and monitoring content directly applicable to their operational responsibilities. Chief Information Officers and IT Directors may benefit from the governance framework discussion as they develop organisational policies around AI adoption.
Compliance and risk management professionals in regulated industries face particular pressure to demonstrate control over AI-related data handling. Financial services, healthcare and other sectors subject to strict data protection requirements cannot easily accommodate the uncertainty that Shadow AI introduces into their data governance posture.
Managed service providers and technology channel partners represent another target audience. As their clients increasingly ask about AI security, partners require technical knowledge and practical frameworks they can apply across multiple customer environments.
The Broader Context of AI Governance
Shadow AI challenges arrive at a moment when organisations are simultaneously trying to capture the productivity benefits of artificial intelligence while managing its risks. The tension between these objectives creates the conditions for shadow adoption. When formal AI procurement processes move slowly or approved tools lack capabilities that employees need, unsanctioned alternatives fill the gap.
Security teams increasingly recognise that AI governance cannot be treated as a simple extension of existing software governance. The speed at which AI capabilities evolve, the ease with which they can be accessed through browser-based interfaces, and the difficulty of distinguishing AI-assisted work from conventional work all require new approaches to visibility and control. This webinar addresses one piece of that larger governance challenge: understanding what AI tools are actually in use and what risks they create.