LLMs introduce unique security risks that traditional authorization methods cannot effectively address
We’ve entered a new phase in how software works. Large language models, or LLMs, don’t behave like the tools we’ve built in the past. They don’t follow strict steps. They don’t wait for fixed command sequences. They operate based on intent, predicting what you want and taking action. Sounds futuristic, and it is, but it also creates a big shift in how we need to think about security.
Traditionally, systems are built around verifying and authorizing users in predefined ways. A person logs in, and the system checks what that person can do. Maybe they can edit files, maybe just read them. It’s predictable, and it’s static. But with LLMs, that model breaks. The AI can call APIs in new ways. It can pull data from unexpected sources. It decides what to do on the fly. The result? You can no longer guess what it will do ahead of time. And if it makes the wrong move, there’s no human in the loop to catch it in real time.
Researchers embedded hidden instructions inside a simple-looking email. Microsoft 365 Copilot, which summarizes email content using LLMs, followed those hidden instructions without a click. It leaked internal company data, not because someone did something wrong, but because the AI did exactly what it thought it was asked to do. No filters. No barriers. Just compliance at machine speed.
That level of access, combined with unpredictability, turns a useful AI into something that could become the perfect insider threat. It can move fast, access everything, and has zero judgment. Traditional security systems don’t cover that. They weren’t built for software that improvises.
And that’s the point: security models have to evolve. Fast. If we keep applying old assumptions to systems this powerful, we’re not just behind, we’re exposed.
Existing AI guardrail techniques provide partial protection but aren’t comprehensive
A lot of teams are already adapting, fast. They’re adding guardrails around LLMs to prevent them from doing something reckless. It’s a good instinct. Developers are creating input filters, output filters, and restricting tool access. These measures help. But they’re not complete solutions. They don’t go deep enough.
Input filters like Rebuff or PromptGuard are designed to catch suspicious requests before they hit the model. They look for known attack patterns, like jailbreak prompts, and stop them. The problem is, attackers don’t stand still. They’ll keep creating new variations, and filters can only catch what they’ve been told to look for.
Output filters, like Guardrails AI or NVIDIA’s NeMo Guardrails, check what the LLM is about to say and block anything inappropriate or dangerous. Again, helpful. But once the LLM has already accessed confidential data, blocking the output is too late. The breach isn’t prevented. It’s just hidden.
Some tools, like LangChain, go another route. They restrict what the LLM has access to from the start, giving it only a limited set of APIs or tools inside a sandboxed environment. That’s good security thinking. But in most real-world systems, data isn’t one-size-fits-all. You can’t just restrict the system at the tool level and assume everything else will fall into place. If you don’t check access at the data layer, you’re open to leaks, even inside your safe little sandbox.
That’s the nuance. These tools are helpful components, but they all depend on the assumption that the AI will behave. That’s a weak foundation to build security on. If we want real protection, especially at scale, we need control over what the AI can see and do, down to the user and record level, every single time it takes action.
That’s the real security challenge in AI. And solving it requires more than filters or restrictions. It requires treating the AI just like any user, with enforceable, real-time permission checks hardwired into every step it takes.
Fine-grained, real-time authorization integrated at the data access layer provides a viable solution
Solving AI security challenges doesn’t come from patching around the edges. It comes from targeting the core of the workflow, where data gets requested and actions get triggered. That’s where fine-grained, real-time authorization matters. It’s the shift from reactive measures to actual control.
One system doing this well right now is Oso. It doesn’t treat LLMs as different from humans in terms of access. If your rights are limited as a user, the AI working on your behalf sees those same limits. Whether the model sends a vector search through a database or makes an API call to pull internal records, every query passes through the same permissions framework. The rules are defined with intention and consistency, down to the specific user, resource, or action.
Technically, the policy infrastructure is written in Polar, and it integrates directly into familiar backend tools like Python’s SQLAlchemy. That means you write the access rule, something like “only managers see department records” or “users only see their own tickets”—and from there, Oso inserts those restrictions into every search or request. That changes the game. Now the LLM never even sees the wrong data. There’s nothing to leak, because unauthorized information was never exposed in the first place.
This approach has already been implemented by companies like Duolingo, Intercom, and PagerDuty. It’s not theoretical. It’s already in production across large-scale, AI-driven products.
Treating AI agents like any other user allows us to apply decades of hard-won security lessons from human access control to machines. The result is reliable, scalable enforcement that doesn’t rely on hoping the AI makes the right call. It reduces risk while enabling the system to run at full capacity, unrestricted by unnecessary barriers, just bound by the rules that keep data safe. It’s the way forward.
Integrating security at the core of AI systems is essential to prevent breaches and support innovation
AI is moving fast, faster than most security systems were designed to handle. But that speed doesn’t excuse cutting corners. Security needs to be part of the foundational structure from day one. If it’s added later, you’re not securing the system, you’re correcting failures after damage is already done.
When security is embedded directly into the core of the AI system, something opens up: trust. The organization can move forward with confidence, not hesitation. Teams can build and deploy innovative AI products while knowing that permission checks, data segmentation, and action gating all work automatically. That kind of setup is especially critical in enterprise environments where information is highly sensitive across teams, geographies, and customer boundaries.
In use cases like retrieval-augmented generation (RAG), where LLMs reference internal documents to create informed answers, you can’t afford to let the model pull results that a user shouldn’t see. By enforcing rules at the point of vector retrieval or SQL query, platforms like Oso make sure LLMs only get access to authorized content before they even begin generating responses.
This isn’t about restricting innovation. Quite the opposite. It’s what enables it. The cost of getting AI-driven access control wrong is far too high to leave to chance. A single leak can cause reputational damage, legal fallout, or massive operational setbacks. But when security is native to the infrastructure, teams move faster with fewer risks.
For C-level leadership, the priority is clear: growth and speed without compromise. That’s entirely possible, but only when security is engineered to operate at the same scale and pace as the AI systems it protects. We know how to do this now. It’s no longer optional.
A collaborative security ecosystem is vital to mitigate AI threats effectively
There’s no single tool that solves AI security. The landscape is too dynamic, and the risks are too broad. That’s why thinking in terms of isolated solutions is counterproductive. What works is a composite approach, building a security architecture where systems reinforce each other, each handling different parts of the problem.
You have tools like Oso delivering real-time, fine-grained authorization tied to specific users and data. You’ve got LangChain limiting AI tool access, ensuring that LLMs can only interact with approved APIs and actions. There are also guardrail libraries that monitor inputs and outputs, providing an extra layer of filtration against misuse or manipulation. Each one plays a critical role, but none is complete in isolation.
What matters for leadership is understanding how these pieces fit together. A well-designed ecosystem doesn’t just block obvious security failures, it anticipates the unpredictable nature of AI agents and distributes responsibility across multiple layers. If an input filter misses an attack vector, permission enforcement on the data layer can neutralize the impact. If output monitoring flags something unusual, logging systems can capture the behavior for review. These systems don’t overlap, they coordinate.
C-suite leaders should expect this kind of layered architecture from their AI infrastructure teams. It’s a minimal expectation for any enterprise deploying LLMs in production. Not every threat is foreseeable, but when your defense strategy is modular and redundant, you reduce the blast radius of anything that slips through one segment. That’s practical, achievable resilience.
Security isn’t just about keeping threats out. It’s about keeping control of what systems can see, do, and produce, no matter the complexity or autonomy of the agents operating them. And the only realistic way to achieve that with LLMs is by leveraging a connected ecosystem of purpose-built tools, each enforcing a specific layer of governance in real time. That’s the strategy that scales.
Key takeaways for leaders
- LLMs require a new security model: AI agents act autonomously and interpret intent, breaking traditional authorization frameworks. Leaders should prioritize systems that validate actions dynamically, not just authenticate users statically.
- Guardrails alone are not enough: Input filters, output monitors, and tool constraints offer partial coverage but still depend on LLMs behaving as expected. Enterprises must avoid relying on these as standalone defenses.
- Fine-grained real-time authorization is crucial: Enforcing user- and data-specific permissions at the query or action level ensures LLMs can’t access or expose unauthorized content. Decision-makers should integrate solutions that apply policies automatically at runtime.
- Security must be embedded into AI design: Retrofitting access control after deployment is high-risk. To enable innovation without compromise, leaders must demand that permission logic is built into AI workflows from the beginning.
- Defense requires a layered ecosystem: No single tool can secure AI systems reliably. Executives should implement a coordinated strategy involving authorization engines, tool limiters, and behavioral guardrails to reduce risk at every layer.
