You can only defend yourself against something you can see or are aware of.

What if something malicious silently exists beyond your visibility or awareness? How would you defend?

AI has already entered organizations, and it isn’t through approved channels.

It is through a browser extension installed on a Tuesday afternoon, a Python script that the developer pushed quietly to production, or through a personal GitHub Copilot subscription used on a corporate device.

The worst part? Most security teams don’t know about it.

Through this blog, we will look at the different types of Shadow AI threats and how STRIEGO-powered SOC helps organizations defend against them.

Shadow AI: A silent threat inside your perimeter

While organizations focus on using AI to boost productivity, the real problem of the huge gap between AI adoption and oversight is often not talked about.

There has been a rise in Shadow Gen AI traffic over the past few years, but organizations only discovered they had a problem when something went wrong.

One in five organizations reported becoming a victim of a data breach due to Shadow AI, and only 34% have governance policies to manage AI or to detect Shadow AI (IBM Cost of Data Breach 2025).

The real problem is that most organizations don’t have the visibility of Shadow AI threats.

It is not a single threat but a category of risks, each with its own profile, detection surface, and characteristics. Let us look at each of them:

Browser-based access

Employees accessing publicly available AI assistants using personal accounts is the most visible threat and significantly increases the risk of exposure of sensitive data.

68% of enterprise employees who used GenAI at work reported using GenAI assistants like ChatGPT, Microsoft Copilot, or Google Gemini using personal accounts (Menlo Security “How is GenAI shaping modern workspace”, 2025).

AI-enabled developer and IDE tooling

GitHub Copilot, Cursor, Rito, Windsurf, Tabnine, Amazon Code Whisperer, and VS Code’s embedded Copilot extension operate as background services that continuously relay code to external interference endpoints. What makes them dangerous is that without triggering any signals, they transmit proprietary algorithms, credentials embedded in the code, internal API schemes, and unreleased business logic. Harmonic’s research found that code was the most common type of sensitive data sent to Gen AI tools.

API driven and application-embedded Shadow AI

Without any human oversight, AI integrations, pipelines, and scripts can silently expose sensitive business data through APIs of LLMs. Since these requests appear as normal HTTP traffic, they often bypass traditional web filtering, CASB, and DLP controls and increase the risk of large-scale data exposure.

AI-browser extensions and sidebar tools

AI-based browser extensions continue to elevate risks of data exposure as employees use unapproved AI extensions. Browser extensions like Sider AI, Monica AI, Merlin, and Max AI automatically access and relay data (like webpage content, documents, and text inputs) to external LLM services without users needing to manually copy data.

Local and on-device AI

Local AI tools like Ollama, GPT4All, LocalAI, Jan, and LM Deploy fully run LLMs on endpoints without generating any external network traffic. Since they don’t generate any traffic, they are invisible to every network layer control, like DNS, firewall, and URL filtering deployed in most enterprise environments. These local AI tools pose a significant Shadow AI threat, especially when consumer-grade hardware becomes more capable of running models locally.

Why do existing controls fail?

Traditional security tools like firewalls, DLP, web proxies, and CASB were not designed to detect Shadow AI. Since AI requests use standard HTTPS, approved SaaS apps, browsers, and trusted cloud infrastructure, they appear as legitimate traffic. Also, sensitive data is often transmitted as structured API payloads that are challenging to detect via traditional detection tools. The real challenge isn’t about tools. It is that most organizations lack the AI-specific visibility, telemetry, and detection capabilities.

How STRIEGO-powered SOC helps prevent, detect, and respond to Shadow AI and AI-driven threats?

SharkStriker helps organizations prevent Shadow AI threats through a team of cybersecurity and compliance experts who help prevent and manage Shadow AI risks.

Through its multi-tenant open-architecture STRIEGO platform, it delivers complete visibility and direct control organizations need for a proactive and resilient posture against Shadow AI threats.

Here are some ways through which STRIEGO-powered SOC helps prevent Shadow AI threats:

Direct Endpoint Action via EDR

SharkStriker’s team takes multiple actions for endpoint-level security against Shadow AI threats, like:

• Blocking of AI applications upon approval
• Configuration and enforcement of policies for sanctioned use of AI tools

Sanctioned vs Unsanctioned AI Separation

All the detected AI activity across infrastructure is compared against the approved tool registry to prevent unauthorized activity. Organizations also get the option to manually approve, restrict, or block specific tools.

Behavioral Baselining

Usage baselines are created based on detection telemetry to identify anomalous AI activity for preemptive investigation and risk mitigation. Anomalous AI activity examples:

• After-hours access to AI services
• Unexpected spikes in inference API traffic
• New AI processes appearing on critical systems

Early Stage Endpoint and Developer Visibility

STRIEGO detects newly installed AI tools and libraries before they begin communicating online, giving the security team time to review and govern tools before they expose data.

AI Governance and Policy Enforcement Support

SharkStriker helps organizations stay compliant with internal policies and evolving AI governance requirements, NIST AI Risk Management Framework (AI RMF), Cloud Security Alliance guidance, ISO 42001, and evolving regulatory expectations under frameworks such as the EU AI Act and other regional AI governance initiatives) through:

• Continuous visibility of AI usage
• Risk assessment of status quo AI tools and
• Quick identification, monitoring, restriction, and blocking of unauthorized AI apps

Measures taken by SharkStriker to detect and respond to Shadow AI risks and threats:

• AI service discovery through DNS, URL, API, and endpoint telemetry monitoring.
• Detection of sanctioned and unsanctioned (Shadow AI) tool usage across the environment.
• User and system attribution for the identification of the source of AI activity.
• Generation of alerts and tickets when Shadow AI activity or unauthorized AI usage is detected (STRIEGO).
• Centralized dashboards and reporting to support AI governance, visibility, and risk assessment (STRIEGO).
• Tailoring of security advisories & recommendations for organizations that have integrated AI in business processes, developer workflows, or (&) applications.
• AI-related threat intelligence sharing based on emerging risks and attack trends to help clients strengthen their security posture.
• Implementation of detection rules and operational workflows for investigation and response to unauthorized AI activity.
• Creation and enforcement of Shadow AI response playbooks based on testing and multi-sourced threat intelligence.
• Threat response based on a comprehensive risk and impact assessment of the status quo AI tool stack.

If you need help assessing & improving your current security and compliance posture against Shadow AI threats, get a call scheduled with our team.