Yesterday I spoke with a team that is responsible for testing 1000s of controls a year. They have an entire staff that goes through the process of gathering evidence, documenting results, and reporting them to leadership day after day.
They work mostly in excel spreadsheets and port the results over to PowerPoint presentations that go to leadership.
It's grueling work, but they are a large healthcare company and this kind of work is important to meet their compliance requirements.
But I am confident this way of working is about to change.
If your team is responsible for testing hundreds or thousands of controls every year for SOC 2, SOX, internal audit, or continuous controls monitoring, things are about to change.
For years, control testing has relied on large teams of internal auditors, consultants, and assessors manually collecting evidence, reviewing screenshots, validating control performance, documenting exceptions, and packaging results for leadership or external auditors.
That model is expensive. It is slow. And, in many cases, it forces highly capable people to spend a large portion of their time doing repetitive administrative work.
But AI is beginning to change the economics of control testing.
And for GRC, internal audit, and compliance teams, that shift creates both disruption and opportunity.
I speak with teams like this all the time that are responsible for monitoring their company’s compliance posture.
In practice, that usually means testing hundreds or even thousands of controls each year across frameworks and programs like SOC 2, SOX, ISO 27001, internal audit, vendor risk, and continuous controls monitoring.
The work is important.
Companies need reliable assurance that controls are designed appropriately and operating effectively. Leadership needs visibility into risk. External auditors need documentation they can rely on. Customers, boards, and regulators expect the business to maintain a strong compliance posture.
But the way this work has traditionally been done is incredibly manual.
Teams collect evidence from system owners. They review screenshots, exports, tickets, logs, approvals, configuration settings, and policy documents. They compare the evidence against control requirements. They document whether the control passed or failed. They write testing narratives. They follow up on gaps. Then they package everything into a format that leadership, auditors, or customers can understand.
At scale, this takes a massive amount of human effort.
I remember working with one organization that had an entire floor of PwC internal auditors testing controls all year round. That was not unusual. Many companies have spent millions of dollars annually on outsourced control testing, internal audit support, and compliance evidence review.
For a long time, that was simply the cost of doing business.
But that model is not going to survive in its current form.
The traditional approach to control testing has three major problems.
First, it does not scale efficiently. As companies grow, their control environments become more complex. More systems, more products, more teams, more regulatory obligations, and more customer requirements all lead to more controls and more evidence. The default answer has usually been to add more people.
Second, it consumes valuable talent on low-leverage work. Many GRC and internal audit professionals did not enter the field because they wanted to chase screenshots, reconcile spreadsheets, or write repetitive testing language all day. They wanted to solve risk problems, build better programs, advise the business, and improve how the company operates.
Third, it is expensive. Large-scale control testing programs often require significant internal headcount, outside consultants, or both. For companies testing thousands of controls every year, the cost can easily reach into the millions.
This creates a frustrating reality: organizations spend enormous amounts of money to perform work that is necessary, but much of that work is repetitive, rules-based, and increasingly automatable.
That is exactly the kind of work AI is positioned to transform.
At risk3sixty, we have built an AI ecosystem designed to fundamentally change how control testing gets done.
The goal is not to remove human judgment from the process. The goal is to remove the repetitive manual work that prevents GRC and internal audit teams from operating at a more strategic level.
AI can now support core parts of the control testing lifecycle, including:
This is where platforms like fullCircle become powerful. Instead of relying on large teams of people to manually inspect every piece of evidence, organizations can use AI-enabled workflows to perform the first layer of review, flag issues, and escalate the right items to the right people.
That changes the role of the human.
Instead of being the person buried in evidence, the GRC professional becomes the architect of the process. They design the control testing strategy. They configure the workflow. They define quality standards. They review exceptions. They evaluate risk. They communicate results. They improve the system over time.
That is a much higher-value role.
I've talked about this before, but this shift is part of a broader transformation happening across the GRC profession.
The market is moving away from large teams doing manual compliance work and toward leaner, more technical, more strategic teams that know how to design and operate modern risk and compliance programs.
The future belongs to the GRC architect.
A GRC architect is not just a compliance analyst who checks boxes. They understand controls, systems, evidence, risk, business processes, audit expectations, and technology. They know how to translate compliance requirements into operating models. They know how to use automation and AI to make programs more efficient, more reliable, and more scalable.
This is a major career opportunity.
The professionals who learn how to use AI effectively will become dramatically more valuable. They will be able to manage larger programs with fewer resources. They will be able to provide better insights to leadership. They will be able to spend less time on repetitive testing and more time improving the company’s actual risk posture.
That is good for the profession.
It gives GRC and internal audit professionals a path out of manual task work and into program design, systems thinking, and strategic leadership.
This does not mean audit, compliance, or consulting teams disappear.
But it does mean the old staffing model is going to change.
Historically, many organizations solved control testing problems by throwing people at them. If there were more controls to test, they added more consultants. If evidence volume increased, they added more reviewers. If deadlines were tight, they added more bodies.
AI changes that equation.
The future model will likely involve smaller teams of highly capable professionals supported by AI-powered platforms, like fullCircle. These teams will still need expertise. They will still need judgment. They will still need to understand audit quality, evidence standards, control design, risk, and business context.
But they will not need to manually perform every repetitive step themselves.
This creates pressure on traditional consulting models that depend on large teams performing manual control testing. It also creates opportunity for firms, internal audit departments, and GRC teams that are willing to rethink how the work gets done.
The winners will be the teams that combine domain expertise with AI-enabled execution.
The most exciting part is that this is not hypothetical.
I am seeing it firsthand in demos and conversations every day. This is not vaporware. This is not a distant future concept. These capabilities are already changing how control testing can be performed.
AI can review evidence. It can evaluate test attributes. It can draft conclusions. It can identify when something does not look right. It can escalate uncertain cases to humans. It can help teams move faster while maintaining oversight and quality.
We use these tools everyday and I'm amazed by the results.
That does not mean every control can or should be fully automated. Some areas will always require human judgment, professional skepticism, and context. But the percentage of work that can be accelerated or augmented by AI is significant.
And that percentage will only grow.
Continuous controls testing has been an aspiration for years.
Many organizations want a world where control performance is monitored in near real time, issues are identified quickly, and compliance reporting is always ready. But the manual nature of testing has made that difficult to achieve.
AI makes the vision more realistic.
Instead of testing controls once or twice per year through a large manual audit cycle, companies can move toward more continuous review models. Evidence can be collected more frequently. Control performance can be evaluated more consistently. Exceptions can be surfaced earlier. Leadership can get better visibility into compliance posture throughout the year.
This has major implications for SOC 2, SOX, internal audit, and broader compliance programs.
It means less fire-drill preparation before audits. It means fewer surprises. It means better data. It means teams can shift from reactive compliance management to proactive risk management.
That is where the profession needs to go.
Will this be a big change for the profession?
Yes.
Some traditional roles will be disrupted. Large teams built primarily around manual testing will become harder to justify. Organizations will expect more leverage from their GRC, audit, and compliance functions. Professionals who resist the shift may find themselves stuck in an outdated model.
But the opportunity is enormous.
AI can help companies reduce waste, improve control visibility, and redirect resources toward higher-value work. It can help GRC teams become more strategic. It can help internal audit teams focus more on risk and less on administrative testing. It can help compliance professionals build better careers.
The future of control testing is not about replacing expertise.
It is about giving experts better tools.
The teams that embrace this shift will not just test controls faster. They will redesign how compliance programs operate.
And that is a much more exciting future for everyone involved.