AI Is Already in Your Org—Are You Securing It All?

It’s been impossible to avoid the buzz around generative AI, especially since ChatGPT took the world by storm. And while tools like DeepSeek, Mistral, and LLaMA are reshaping the open-source frontier, one thing is certain: generative AI is here to stay. While the productivity gains are real, so are the risks—particularly when security teams lack visibility or control over how these tools are used.

This isn’t a theoretical future, it’s happening now. GenAI is already embedded into your organization, whether through unmanaged consumer apps, enterprise SaaS integrations, or models developed in-house. And unless you’re actively managing each of those domains, you’re likely flying blind.

The Three Faces of AI in the Enterprise

Not all AI is created equal. To protect your data and users, it’s essential to understand the different types of AI your organization interacts with,and the unique risks each one brings:

Unmanaged third-party AI: Tools like ChatGPT, Claude, DeepSeek, and Google Gemini fall into this category. They’re easy to access and often free, many employees are opting to purchase subscriptions themselves for the productivity boost.However, these tools may come with unclear governance and unknown data handling practices. Users may unknowingly expose sensitive IP or regulated data through casual usage.
Managed second-party AI: Generative AI is increasingly integrated into SaaS apps— everything from customer support platforms to marketing tools as well as enterprise versions of the tools above. While these apps may offer enterprise controls, they also expand your AI footprint in ways that may not always be visible or fully understood by security teams.
Homegrown first-party AI: Whether you’re fine-tuning open-source models, building classical ML pipelines, or using “AI Studios” provided by well known enterprise vendors, in-house AI introduces a host of operational and security responsibilities: from safeguard configuration and drift management to privilege enforcement and compliance.

Real Risks, Already Happening

Regardless of AI type, we see consistent patterns of misuse:

Unaware misuse: Employees pasting confidential data into a chatbot prompt, unaware it may be stored or used downstream—or trusting the model’s response without verification, potentially introducing insecure code or flawed content into business systems.
Unauthorized access: AI apps retrieving or generating content outside their intended scope.
Oversharing: Autocomplete features or summarization models surfacing more data than appropriate.
Unintentional public exposure: Poorly configured APIs or models exposed without appropriate authentication or authorization.
Misconfigured safeguards: Logging, auditing, and access controls missing or broken.

These aren’t just theoretical risks, they’re already emerging in real-world incident investigations. Adversaries are also exploiting public models for social engineering, data extraction, or testing the limits of enterprise-grade defenses.

The Open-Source Temptation

With the rise of powerful, freely available models like Mistral, LLaMA, DeepSeek, etc. many organizations are exploring open source as a cost-effective, customizable alternative to closed platforms. But that flexibility comes at a cost.

Open source means you own the full lifecycle: from model evaluation and alignment to ongoing monitoring and access control. Without rigorous safeguards, these models can become shadow assets—exposed, over-permissioned, and under-observed.

Before deploying any open-source model, teams should ask:

How was the model trained? On what data?
Can we audit its outputs and behaviors?
Who has access to it, and how is that governed?
Can it be fine-tuned securely without data leakage?

Understanding “Open” in Open Source AI

The term “open source” is often used broadly, but in the context of AI, it can mean several distinct things:

Open weights: The model parameters are available for download and use, but the codebase, training data, or licensing may still be restricted. Security implication: anyone can run the model, which increases the risk of abuse or uncontrolled deployment.
Open source: The code used to train and operate the model is publicly available and modifiable. Security implication: transparency allows for peer review, but also opens the door for adversaries to analyze and exploit weaknesses.
Open data: The dataset used to train the model is publicly shared. Security implication: if not properly sanitized, it can include toxic, biased, or even sensitive content that may be regurgitated by the model.
Open training: The entire training process is reproducible and transparent, including scripts, configurations, data, and compute settings. Security implication: high reproducibility fosters trust, but also enables replication by malicious actors.

Each of these dimensions introduces different threat surfaces. For example, open data may expose your organization to reputational risk or regulatory scrutiny if sensitive examples were included. Open training can make your model architecture and behavior predictable—a double-edged sword in adversarial environments.

Organizations adopting open models must therefore implement controls across the entire lifecycle: secure hosting, permissioned access, prompt monitoring, and active threat detection.

Secure the Full AI Surface

As agentic AI continues to gain momentum, a new dimension of security is emerging: safeguarding not just human interactions, but agentic ones. These AI agents are increasingly capable of performing complex, multistep tasks—sometimes assisting humans, and sometimes operating independently without oversight or approval. This shift from human-only to human+agent workflows creates new trust boundaries and risk scenarios.

In the near future, agents may draft and send communications, manipulate data across systems, or initiate transactions without human involvement at all. While this promises greater efficiency, it also introduces questions around identity, authorization, auditing, and escalation.

To keep up, human-centric security must evolve to cover this expanded model:

How do you authenticate and authorize agents?
Can you detect anomalous agent behavior in real time?
Are agents operating within their defined scopes and privileges?
How do you ensure agents are not being misused—or compromised?

Enterprises must start treating agents as first-class security entities, with clear controls, policies, and accountability mechanisms in place.

Generative AI has transformed how we work, but it has also reshaped the data surface that security teams must defend. Securing AI isn’t just about managing a single vendor or use case—it’s about understanding how different types of AI operate, how users interact with them, and how data flows through each.

Your AI strategy must start with an AI inventory, followed by a risk-based policy framework. Who is using what? What data is being shared? Are access controls appropriate? Are detections in place for misuse?

AI security requires visibility into user behavior, deep inspection of data flows, and enforcement of policy at the edge and beyond. This is the new perimeter, and it demands a new playbook.