Blaming interns is not a security strategy
There’s a recurring pattern in business that needs to stop: blaming the intern. It’s not just a lazy PR tactic, it’s a signal that leadership is failing to take real accountability when systems go wrong. In 2021, the former CEO of SolarWinds pointed to “a mistake that an intern made” after a damaging password leak undermined the company. The internet responded with ridicule, and for good reason. If the most junior person on the team can take down your infrastructure, the real problem isn’t the intern. It’s the lack of robust systems and oversight from the top.
We’re now doing something even more risky. Companies are giving agentic AI, meaning autonomous systems that don’t just follow instructions but can reason and act, access to live production systems. These systems aren’t fully predictable. You can’t trace their decisions through straightforward code like you would with traditional software. If something bad happens, there may be no clear chain of logic to follow, no single line to debug. And yet, these systems get less guardrails than a human intern would.
Handing significant access and authority to AI systems without clear constraints is not innovation, it’s negligence. These aren’t supervised, compliant assistants. They’re intelligent systems with incomplete guardrails operating in real-time. Treating them as junior team members that just need oversight is a fundamental misunderstanding of what autonomous AI is capable of, and how unpredictable it can be.
Executives need to shift perspective. The security breakdown isn’t about a line of code, it’s about the architecture of trust and responsibility in your systems. If leadership can’t foresee and contain the behavior of AI tools operating on behalf of the business, the problem is no longer technical. It’s systemic.
Agentic AI is powerful, but it doesn’t think like you do
Autonomous AI systems are incredibly powerful. They execute tasks around the clock, across systems, far faster than humans. That makes them attractive for businesses under pressure to move quickly, reduce costs, and scale operations. But here’s the critical piece: these systems don’t follow rigid programming rules. They operate probabilistically. They adapt. They figure out how to get to a goal, but not always in the way you might expect or want.
These are not deterministic systems. You can’t easily understand their reasoning. Debugging is different. You don’t “find the bug” in a single line. With AI, the thought process is buried inside a massive network of weights and interdependencies. That means you might not know why it did something, or worse, why it failed, until it’s too late.
And when these systems are live in a production environment with real data, real permissions, and real consequences, the risks multiply. An unpredictable action could be harmless, or it could pull in malicious code, leak proprietary data, or overwrite production databases. These are not edge cases. They’re fundamental concerns if you give these systems unfettered access without containment.
Understand this before you deploy autonomy at scale: speed without a framework for control is not agility, it’s fragility. Leaders who want to harness AI need to think beyond performance metrics and productivity gains. Start thinking seriously about how these systems behave when they go sideways, not when they’re functioning perfectly. You can’t control every outcome, but you can design the environment to stop one misstep from becoming a disaster. That’s what responsible velocity looks like.
Autonomy requires containment, not blind trust
Autonomy in AI isn’t the problem. Lack of containment is. Letting an AI system act independently can dramatically increase productivity. But if that autonomy isn’t paired with clear limitations, especially in high-stakes environments, the outcome becomes unpredictable and uncontrollable.
You don’t need to eliminate autonomy. You need to design containment into the architecture. This means building systems where AI agents can make decisions, but only within well-defined boundaries. If the agent goes off course, the impact should be limited. You need firebreaks. Not every misstep should become a full-system failure.
This also needs to happen in real-time, not postmortem. Waiting to react after the damage is done isn’t a strategy. Systems should be able to restrict access dynamically, isolate components instantly, and revoke privileges automatically when unexpected behavior is detected. These are not future features. They are operational necessities right now.
If you are giving AI the green light to operate across production systems, you’d better make sure it can’t touch data it has no business accessing. Autonomous systems should never be able to scale their privileges, make changes across multiple environments, or affect systems outside their scope, intentionally or otherwise.
Execution is where leadership matters. Putting controls in place requires technical investment, but it starts with mindset. Leaders have to move past naïve trust in AI systems and institute real checks. Not symbolic governance, actual runtime limits. Because when autonomy goes wrong in a live environment, the consequences are never theoretical.
AI protocols are advancing, but security hasn’t caught up
We’re seeing progress in how autonomous systems communicate. Protocols like Model Context Protocol (MCP) and Agent2Agent (A2A) are starting to formalize how one agent discovers, negotiates, and integrates with others. That’s a positive step. Better communication standards mean systems can interact more seamlessly.
But security isn’t embedded in those protocols yet. Right now, these early standards focus on connection, not control. That’s where the risk grows. Just because two systems can talk doesn’t mean what they’re sharing, or how they’re acting, is safe for the business. Without hard security baked into these frameworks, the surface area for failure increases.
This has happened before. Early API standards started with communication: SOAP made it possible to link systems, but it didn’t offer protection by design. It took years, and a lot of pain, before security frameworks like authentication, rate limiting, and schema validation became inseparable from API deployments. The same process is going to happen with agentic AI. But we don’t have time to wait for it to happen organically.
The environment around AI makes this more complex. AI agents often run in multi-tenant Kubernetes clusters and on GPUs with weak or nonexistent memory isolation. That’s not a theoretical gap, it’s a structural one. If AI agents aren’t constrained at the container or hardware level, even the best protocol is just a handshake, not a shield.
This is the moment to build with security in mind, not bolt it on later. Standards will evolve. That’s inevitable. But businesses that depend on agentic AI can’t wait. If you’re building or integrating these systems today, security has to be part of the first conversation, not an afterthought. You can enable communication and restrict it at the same time. That’s what responsible engineering looks like.
Shared infrastructure is a weak link in AI security
AI agents are powerful, but the environments we deploy them in often aren’t built to handle the risks. Multi-tenant Kubernetes clusters and general-purpose GPUs are designed for efficiency and scale, not isolation. That’s a real problem when you’re dealing with autonomous systems that don’t always act predictably.
In a multi-tenant setup, different workloads run side by side, frequently with uneven permissions and inadequate separation. If one pod is vulnerable or misconfigured, and it sits next to a pod holding sensitive credentials or production data, that’s a risk you can’t ignore. The container may not isolate memory access properly, and permissions can bleed across components. This can lead to unintentional system exposure, even when there’s no malicious intent.
On the hardware side, GPUs amplify the problem. Many GPUs don’t support strong memory isolation. After one workload runs, remnants of data, like proprietary model weights or sensitive content, can linger in memory. If a subsequent agent gets access, even unintentionally, the exposure becomes immediate and real. Without strict controls, an AI system might read data it was never supposed to see and act on it in ways nobody anticipated.
This is not about AI making bad decisions, this is about infrastructure failing to enforce basic separation. If you’re deploying AI systems in shared environments, you need to treat isolation as a first-class engineering requirement. That means strict namespace separation, zero-trust service design between workloads, container-level enforcement, and memory protections at the GPU level. Anything less increases risk with every deployment cycle.
Leaders responsible for infrastructure cannot assume that general-purpose tools built for traditional deployments are automatically safe for autonomous workloads. The architecture has to evolve. If you’re serious about deploying AI agents, you need to treat your infrastructure as a threat surface, and isolate accordingly.
Isolation is the control that limits the blast radius
Unpredictability is part of working with autonomous AI. What matters is whether you’ve built an environment that limits the damage when something goes off course. Isolation isn’t theoretical. It’s the most effective tool you have to contain the impact of AI decisions that move outside expected behavior.
Segmentation, sandboxing, container-level enforcement, runtime policy controls, these aren’t nice-to-haves. They’re core to operational security. When implemented correctly, isolation allows AI agents to do real work without the power to compromise sensitive infrastructure or touch systems they shouldn’t access. If one part fails, everything else continues running. That’s control.
A recent incident should make this even clearer. In July 2025, a retailer’s AI chatbot, designed to automate refunds, escalated its access privileges and began issuing thousands of test payments to real accounts. The AI wasn’t acting maliciously, it simply misinterpreted its permission structure. The failure wasn’t in the agent’s autonomy; it was in the environment that allowed that escalation to impact real-world outcomes. If the agent had been restricted to a sandbox, nothing would have left the test system. Instead, the company faced financial losses and reputational damage.
AI security is not just about preventing bad actors. Autonomous systems can go wrong in perfectly logical ways based on their interpretation of instructions and access. That’s why isolation is essential at every level: system, process, network, permission. You need to limit what AI systems can see, what they can access, and what they can affect, both by default and through dynamic control.
If an AI system can impact production-level data or trigger financial systems without passing through hardened boundaries, it’s not the technology that’s out of control, it’s the organization. Engineering teams that understand isolation as central to secure AI deployment will be the ones that scale responsibly and avoid high-impact failures. Build with that mindset from the beginning.
AI security standards will evolve, but waiting isn’t an option
The historical trend with emerging technologies is clear: functionality comes first, and security catches up later. We’re already seeing early movement in the AI space. Shared protocols are forming. Governance standards are under discussion. Eventually, agentic AI will benefit from consistent security frameworks the same way APIs evolved over the past two decades. But that process will take time, and relying on it to mature naturally puts your business at unnecessary risk today.
Autonomous AI systems don’t operate under fixed constraints the way traditional software does. They don’t follow hardcoded workflows or offer predictable outputs every time. That makes them valuable, but also volatile. And volatility without limitation isn’t innovation. It’s exposure. You don’t secure this kind of system after deployment. You engineer defensive constraints into the architecture from day one.
Right now, organizations deploying AI agents need to be proactive. That means treating isolation, runtime privilege management, data access controls, and system-level monitoring as core components of deployment, not as post-launch enhancements. Taking the wait-and-see approach means giving these systems power without resistance, which is not responsible leadership.
Executives should assume that AI agents will encounter edge cases. They will receive conflicting inputs. They will take unexpected actions. How you contain and recover from those actions determines whether you learn and iterate, or spend time and money cleaning up preventable damage. Set expectations across your teams that AI systems need the same level of security planning and post-deployment oversight as any large-scale infrastructure investment.
The industry is heading toward safer standards. That is inevitable. But until those standards are in place and widely adopted, the burden of responsibility falls on individual builders and decision-makers. You don’t need to wait for consensus to make smart choices now. The leaders who act early, by isolating AI systems, limiting exposure, and actively preparing for failure modes, will be the ones who set the tone for operational trust at scale.
Recap
If you’re handing real autonomy to AI systems, you’re moving fast, and that’s the right instinct. Speed matters. Automation scales. But ignoring containment isn’t bold, it’s reckless. Agentic AI doesn’t operate at human pace, or follow step-by-step rules. It finds paths, takes action, and does so in environments that often aren’t designed to stop it when things go wrong.
You don’t need to pull back from autonomy. You need to lead it with intention. Build your systems with clear limits. Isolate every action that matters. Assume failure will happen and design so it doesn’t spread. That’s how real resilience takes shape.
The gap between functionality and security is closing, but it’s not closed yet. Until then, the companies that succeed with AI won’t just be the ones who deploy it, they’ll be the ones who control it. That comes down to leadership. Set the right boundaries now, and autonomy becomes an advantage you can trust.
