Tl;dr

• At Aim, we see the world of enterprise AI divided into two realities: the AI you use and the AI you build.
• The AI you use — public and SaaS tools like ChatGPT, Gemini, and Copilot — creates data-in-motion risks such as Shadow AI, data leakage, and compliance violations as employees interact with external models.
• The AI you build — internal copilots, chatbots, and autonomous agents — introduces execution and runtime risks, from prompt injections and context poisoning to malicious model files that compromise business systems.
• Treating these as the same risk category creates blind spots and a false sense of control. Each requires a distinct security strategy.
• Enterprises must use AI-specific guardrails and visibility to secure the AI they use, and lifecycle protection, continuous model scanning, and runtime defense to secure the AI they build.
• In short: you can’t secure what you don’t classify — knowing which AI you’re dealing with is the first step toward safe, scalable innovation.

AI has become the new enterprise operating layer. From employees using ChatGPT or Gemini to accelerate their workflows, to teams building copilots, custom chatbots, and autonomous agents, the lines between productivity and risk have never been thinner. Most organizations are still securing AI with a single playbook. They treat every AI interaction — whether through a public model or a proprietary system — as the same category of risk. It isn’t.

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The AI you use and the AI you build represent two fundamentally different security realities. Each introduces unique vulnerabilities, regulatory obligations, and attack surfaces. Treating them the same leads to gaps and gives a false sense of control.  In this post, we’ll break down these two AI worlds, explore their distinct risks, and outline practical strategies for securing both — so security leaders can enable innovation, not obstruct it.

The AI You Use: The External Frontier

The AI you use refers to public, third-party, and SaaS-based tools — ChatGPT, Gemini, Claude, Copilot, and hundreds of others that employees are now using daily. They sit outside your security perimeter, process data you don’t control, and evolve faster than your policies.

Shadow AI is the unsanctioned, unmanaged use of generative AI across the enterprise/ It isn’t malicious, it’s human. Employees copy text into ChatGPT to save time. Developers use copilots to debug faster. Marketers experiment with Gemini for content creation. Each of these actions introduces data-in-motion risk: intellectual property, customer information, internal roadmaps, or proprietary code flowing out of your environment into models you don’t control. These models may log, cache, or reuse it in ways your organization can’t see or control. Aim’s research shows that most enterprises underestimate Shadow AI adoption by 5–10x, with thousands of untracked AI interactions happening every day across browsers, chat tools, and copilots

Even when organizations use “enterprise” versions of public models, risk remains. Enterprise LLM tiers often promise data isolation, but not all contracts or implementations are equal. Logs may persist for operational metrics, and employees frequently blur the line between corporate and personal accounts.

The AI You Build: The Internal Frontier

The second world is the AI you build: your own, LLM-powered chatbots, autonomous agents. You control the model, but the risks are different. Unlike public LLMs, your homegrown AI lives inside your infrastructure, integrated with business systems, APIs, and proprietary data. That means every vulnerability is now your problem.

Attacks like prompt injections, context poisoning, or malicious model files don’t just leak data — they can trigger real actions, manipulate workflows, or even compromise downstream systems.

This is no longer about end-user misuse. It’s about runtime exploitation, supply-chain risk, and compliance by design. Securing this world requires continuous testing, behavioral model scanning, and real-time runtime protection — not policy enforcement.

Aim Labs’ findings on prompt and model attacks show how even small misconfigurations or unscanned open-source models can expose entire pipelines to exploitation.

Why the Distinction Matters

Understanding the difference between the AI you use and the AI you build isn’t semantics — it’s strategy. The AI you use lives in your employees’ browsers. It leaks data, bypasses policy, and challenges governance. The AI you build lives in your infrastructure. It executes actions, integrates with your systems, and, if exploited, can compromise entire workflows. Treating these realities as one blurs accountability and blinds security teams to the unique risks each introduces.

Both are vital. Both are growing. But they require different toolsets, processes, and ownership.

The Risks That Hide in Plain Sight

1. Data leakage – Employees paste sensitive corporate or customer data into third-party models. Once shared, that data is outside your control and may reappear in model outputs or be retrievable through prompt injection. Aim Labs’ EchoLeak research, for example, demonstrated how data from integrated copilots could be extracted by malicious prompts. ‍
2. Compliance violations – Many jurisdictions now regulate how AI can process personal or employment-related data. In New York City, Local Law 144 restricts AI-driven hiring decisions; in the EU, the AI Act governs how personal data may be used in training and inference. Untracked AI usage can easily breach these rules. ‍
3. Unvetted applications – Beyond headline tools like ChatGPT, thousands of consumer-grade GenAI apps handle text, images, and code. Most haven’t been evaluated for data handling, model lineage, or security posture. Blocking them outright doesn’t work; employees simply find alternatives. ‍
4. Policy drift – Even sanctioned tools introduce risk when used inconsistently across departments. Marketing’s use of Gemini may follow compliance guidelines, but a developer’s use of the same model in a code environment may not.

What begins as innovation quickly becomes exposure — and without visibility, CISOs are flying blind.

Innovation Expands the Attack Surface

Homegrown AI introduces risks that traditional AppSec frameworks don’t cover.

• Prompt injections can subvert instructions and trigger unauthorized actions.
• Context poisoning can manipulate RAG (Retrieval-Augmented Generation) pipelines by injecting malicious content into knowledge bases. ‍
• Malicious models downloaded from open-source repositories can contain hidden code that executes during loading — as Aim Labs’ Dynamic Model Scanning research revealed.

These aren’t theoretical. Researchers have already demonstrated that a single compromised model file can exfiltrate secrets, modify downstream systems, or trigger system commands when loaded into a production pipeline. See CurXecute, & ExcoLeak. And as agentic AI — systems that act autonomously — gain traction, risk multiplies. Agents that can read internal data, communicate externally, and execute API calls create a “lethal trifecta” of access, autonomy, and exposure.

Why Traditional Controls Fall Short

Most enterprises rely on static scanning, API authentication, or compliance documentation to secure their AI builds. But these controls don’t account for how AI models behave at runtime. LLMs don’t just process requests — they reason, adapt, and act probabilistically.

Securing this layer requires continuous monitoring of model behavior, context validation, and active runtime defense. In AI, vulnerabilities emerge not from code flaws alone but from behavioral unpredictability — and that demands an entirely different mindset.

How to Secure the AI You Use

Securing employee AI usage starts with changing the security model from “deny” to “design.” The goal isn’t to block AI — it’s to enable it safely.

1. Illuminate the Unknowns

You can’t govern what you can’t see. Start by discovering which AI tools are in use across your workforce — not just ChatGPT, but copilots, plug-ins, and embedded AI assistants inside everyday SaaS tools. This discovery layer should provide a living inventory of AI interactions: who’s using what, how often, and with what data. Aim’s Shadow AI research emphasizes that this visibility is the foundation for every other control .

2. Define Guardrails, Not Blocks

Once visibility is established, the next step is policy enforcement through guardrails rather than hard restrictions. Guardrails operate at the interaction layer — scanning prompts and responses in real time to detect and prevent sensitive data exposure. They also provide contextual feedback, nudging employees to correct risky behavior instead of halting their workflows. As Aim’s AI Security Guardrail Framework describes, the most effective controls balance compliance assurance with user enablement .

3. Measure and Operationalize Adoption

AI governance isn’t static. To sustain secure adoption, organizations need metrics: how AI tools are used, which departments are driving the most usage, and whether policy exceptions align with business outcomes. These insights turn AI from a risk into a measurable productivity driver — transforming “Shadow AI” into sanctioned innovation.

How to Secure the AI You Build

Building AI internally demands a defense-in-depth approach — one that spans from the model’s supply chain to its runtime behavior.

1. Secure the Development Lifecycle

Implement AI Security Posture Management (AI-SPM) to continuously discover and assess every model, dataset, and endpoint across your environment. AI-SPM combines static and dynamic model scanning to identify backdoors, malicious payloads, and licensing issues before deployment . It also maps lineage — where each model comes from, how it was trained, and whether it complies with frameworks like the EU AI Act, MITRE ATLAS, and OWASP Top 10 for LLMs .

2. Protect Runtime Behavior

Once deployed, models and agents must be monitored and controlled at runtime. This is where AI Firewall solutions — like Aim’s proprietary detection engine — come into play .

Runtime protection inspects prompts, context, and outputs in real time to detect:

• Prompt or context injections
• Unauthorized tool calls
• Data exfiltration attempts
• Compliance violations

By applying these guardrails inline, organizations can protect production AI systems without introducing latency or developer friction.

3. Continuously Test and Validate

Finally, organizations must assume that no security measure is static. Continuous AI Red Teaming — simulating real-world attacks, jailbreaks, and manipulations — is critical for maintaining resilience .

Red teaming validates whether your runtime controls and posture management tools work under stress. It identifies gaps before attackers do, ensuring your AI systems evolve securely as threats evolve around them.

Conclusion: One Platform, Two Realities

Every modern enterprise now operates in both worlds. Employees are using AI to work smarter, and engineers are building AI to transform business operations. The challenge — and opportunity — lies in securing both simultaneously.

• For the AI you use: Discover, govern, and measure employee interactions. ‍
• For the AI you build: Protect every phase of the lifecycle — from training to inference to runtime.

The organizations that grasp this distinction and implement tailored strategies for each will be the ones that scale AI safely, responsibly, and competitively.

Because AI isn’t a single technology. It’s an ecosystem — and securing it starts with knowing exactly which AI you’re dealing with.