Why AI governance fails when agents are asked to police themselves
Europe’s AI rules are exposing why autonomous systems need real-time oversight, not more algorithms
Enterprises racing to deploy autonomous AI agents are discovering a structural weakness in today’s governance approaches. Many existing systems rely on artificial intelligence (AI) to monitor artificial intelligence, an assumption that becomes increasingly fragile as AI systems gain autonomy.
Across finance, enterprise software, and critical infrastructure, companies are deploying AI agents that can query databases, retrieve customer information, and automate internal workflows. As these systems become more independent, they introduce risks that traditional compliance tools were never designed to detect.
At the center of the problem is trust. AI systems are non-deterministic, meaning they can misinterpret instructions and act beyond their intended scope. When oversight itself is delegated to another AI system, uncertainty compounds rather than diminishes.
“There are some competitors in the space, and they all tend to use AI to check AI. We’re not doing that because that’s problematic,” Sheraz Yousaf, founder of AI Guardian Systems, told TechJournal.uk in an interview. “AI makes mistakes, so you can’t really trust it to analyze everything 100%.”
Yousaf said delegating governance to AI creates a false sense of control at precisely the moment enterprises need deterministic and auditable safeguards.
“We’re not using AI for this. We’re using a rules-based compliance engine, and then we’ll add machine learning once we start to get more data from companies,” he said. “The market is validated, but it’s not full, and certainly our idea offers a lot of value over what’s already out there.”
Regulation drives urgency
The shift toward stricter AI governance is being driven less by technology and more by regulation. The European Union’s AI Act is transforming AI oversight from a policy exercise into an operational requirement.
“This came about because of the laws with the EU AI Act that have come in,” Yousaf said. “From August this year, high-risk systems will be subject to the laws.”
Under the framework, penalties are no longer symbolic.
“That means the EU AI laws can fine companies up to 7% of their global turnover, or up to €35 million,” he said.
Unlike earlier compliance regimes, the AI Act focuses on how systems behave in practice, not just how they are documented. Companies are increasingly expected to demonstrate ongoing control rather than one-off compliance checks.
“What this will do is take all those compliance laws and map the AI activity to see whether it’s within those laws or not,” he said. “Companies will get an audit log of where they’re in and out of these laws and be able to fix it and prevent those fines.”
As AI systems evolve after deployment, governance is shifting toward continuous oversight rather than static assessments.
When AI drifts
The hardest governance failures are rarely dramatic. Instead, AI systems drift beyond their original intent.
“AI is quite non-deterministic, and it can make mistakes,” Yousaf said. “It might interpret your command incorrectly.”
In enterprise environments, those misinterpretations can expose sensitive information. An AI agent designed to support customer relationship management (CRM) may access internal financial data, employee records, or other restricted information.
“It might go off and start looking at salaries, even though that’s not what it was asked to do,” he said.
Because AI agents chain actions together autonomously, such behavior is difficult to predict in advance. Traditional permission models are often insufficient once an agent is allowed to operate across multiple systems.
“We log it in the live feed as blocked. It doesn’t allow the AI agent to do that,” he said. “The most important bit is that we can see exactly what the AI is doing.”
That visibility, he said, is critical not only for regulatory compliance but also for internal trust and risk management.
AI Guardian Systems positions itself as governance infrastructure rather than another AI model or analytics layer. The system is designed to make enterprise AI deployments audit-ready by monitoring how AI systems behave in live environments, rather than assessing them after incidents occur.
The platform focuses on observing consent boundaries and usage scope in real time, detecting when AI agents drift beyond their intended permissions, and generating compliance logs that can be used for regulatory reporting.
Integration is deliberately lightweight. Companies add a small number of lines of code to their existing AI calls, allowing responses and consent parameters to be monitored as they occur, without requiring system migrations or architectural rewrites.
The platform is also built to monitor multiple AI systems simultaneously, providing dedicated visibility across several AI agents operating within the same organization.
Building governance at scale
Yousaf has spent more than a decade working on enterprise technology transformation in regulated environments, including financial services and national payments infrastructure. His background spans solution architecture, release management, and large-scale system delivery, with a focus on governance, risk controls, and operational resilience.
“I built three different proof-of-concepts. This was the final one,” he said.
The early version tested whether real-time AI monitoring was feasible. The focus has since shifted toward building an enterprise-grade platform, which will be ready in March.
He said AI Guardian Systems, a startup company, now has a head of enterprise sales, a senior front-end developer, and a senior back-end developer.
While large language models (LLMs) were used during early experimentation, production demands a different approach.
“I used Claude to write the code for the proof of concept, and now I have a real development team building the enterprise MVP (minimum viable product),” he said.
Despite regulatory momentum, most enterprises remain early in their AI governance journey.
“I think I’m 12 to 18 months ahead before companies start doing this in a big way,” he said.
Many organizations still rely on manual checks rather than dedicated monitoring systems, an approach that may become increasingly difficult to defend as regulators demand clearer evidence of oversight.
As AI systems gain autonomy, the assumption that machines can safely police themselves is eroding. For enterprises and regulators alike, effective AI governance may depend less on smarter algorithms and more on visibility, rules, and accountability.