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IT teams have always sought ways to streamline routine tasks in the Microsoft ecosystem through scripting. In doing so, they have had to abide by regulations concerned with resilience, data security, and accountability. This is nothing new.
However, there is a tougher nut to crack now in play for both IT teams and regulators: Agentic Automation.
Automating Microsoft ecosystems is rapidly becoming a strategic priority for IT leaders.
Routine IT tasks such as user onboarding, device configuration, and network maintenance no longer require fragmented PowerShell scripting. Instead, an AI-powered automation platform can register incoming requests and trigger follow-up executions across the ecosystem with minimal to no human input.
The result will be drastic workload reduction, faster response times, and lower operational overheads, with higher productivity across the business.
But as AI begins to participate directly in the management of IT workflows, rather than merely standing on the sidelines as an advisor, the risk profile for businesses and customers changes dramatically.
Without strong guardrails, key business infrastructure is exposed not only to errors in AI decision making, but also to malicious actors looking to exploit them.
Therefore, as 82% of companies experiment with automated systems, regulators around the world are starting to ask questions:
- When actions are performed autonomously, who holds accountability?
- How do you ensure that AI-driven automation is safe and aligned with your organization’s information security policy?
- How do you guarantee to stay compliant with regulations such as HIPAA, DORA, NIS2, and GDPR, which all place a high importance on traceability, accountability, and oversight?
Without a clear answer to these questions, your agentic automation can quickly become a serious liability.
In this article, we’ll explore the risks that agentic automation can introduce, and how a centralized automation approach can help your organization stay compliant, secure, and productive in the face of rising regulatory pressure.
What Are the Typical Risks of Agentic Automation That Regulations Are Guarding Against?
If done well, integrating AI-driven automation into your Microsoft automation strategy can deliver immense efficiency. If done poorly, it can quickly produce chaos that puts your infrastructure and sensitive data at serious risk:
Script Sprawl and Shadow Automation
When AI agents are allowed to create and execute scripts automatically, they can do so faster than teams can track them. This can lead to hundreds of undocumented automations running across environments with no clear owner, purpose, or version control.
Unintentional Privilege Escalation
One of the costliest mistakes an AI agent can make is misconfiguring permissions and credentials within your IT infrastructure, leaving privileged access open to malicious actors.
Lack of Clear Audit Trails for AI Prompts
If documentation and logging policies have not been properly updated for the AI era, AI executions are often not logged. This creates a serious accountability gap: who instructed the AI to run, and why?
Unaccountable Decision Making
Unlike human engineers, AI agents don’t fear the repercussions of breaching policy or regulations. They can execute an action that technically solves a problem but violates the compliance standards that ensure that those actions are safe and equitable.
Together, these risks underscore the need for robust governance that preserves the benefits of agentic automation without compromising on transparency, accountability, or control.
This is the kind of governance that regulators are increasingly looking to make mandatory.
The Global Landscape of AI Regulation
Around the world, governments and standards bodies are converging on a shared goal: to ensure that emerging AI systems avoid putting critical infrastructure and sensitive data at risk by staying transparent, accountable, and under meaningful human control.
Here are some examples of global regulations and their requirements:
The EU AI Act and the U.S.’s One Big Beautiful Bill both provide standards for companies deploying AI in their systems, focusing on transparency, privacy, data protection, accountability, and civil rights. In the EU’s case, these standards are legally binding and come with fines of up to €15 million or 3% of global annual turnover for non-compliance.
Meanwhile, the OECD has laid out an AI policy blueprint that has been adopted by 47 countries. This promotes use of AI that aligns with five value-based principles, including sustainability, privacy, transparency, security, and accountability.
Finally, compliance frameworks such as the NIST AI Risk Management Framework, ISO/IEC 42001, and Singapore’s Model AI Governance Framework offer guidelines that focus on internal governance structures, human involvement, traceability, explainability of AI-driven decisions, and robust risk management strategies.
Whether legally mandated or industry-led, these frameworks converge on five universal principles for agentic AI deployment in enterprise-level systems:
- Transparency: You must document and communicate how AI operates and why.
- Accountability: You must assign human responsibility for all AI decisions and actions.
- Traceability: You must maintain detailed logs of data, prompts, outputs, and metadata.
- Security and robustness: You must ensure systems are resilient to misuse, attack, or error.
- Human oversight: You must keep humans in control of high-risk processes.
These principles must be addressed using a robust governance framework built into the foundations of your automation strategy, and not as a compliance afterthought.
4 Steps for Avoiding Risks and Ensuring Compliance
Complying with AI guidelines and regulations doesn’t mean sacrificing the productivity of your automations.
Rather, it means embedding best practices into your automation architecture from the beginning to guarantee ROI, avoiding common pitfalls and costly penalties.
Here’s what that looks like in practice:
Establish a Control Plane for Total Oversight
All agentic automation activity should be routed through a centralized automation platform that acts as a single source of truth for managers, technicians, and auditors. This eliminates shadow automation and script scrawl, providing full oversight of every script and execution across your organization, whether executed by humans or AI agents.
Enforce Strict Credential and Permission Scoping
Like human users, AI agents should operate within strictly defined privilege boundaries. Centralized role-based access controls ensure that no one can execute scripts beyond their intended scope, nor be able to change these permissions by itself.
Ensure Transparency, Traceability, and Auditability
Every stage of automation, from script generation to execution, should generate a comprehensive audit trail. This means logging prompts, inputs, outputs, and execution metadata, so that teams can see exactly how each automation was initiated and its results.
Enable Human-in-the-Loop Controls
When it comes to sensitive data or critical infrastructure, AI may write or suggest automation logic, but final control must remain human. Introducing approval checkpoints for high-impact scripts helps balance speed with safety and holds teams accountable for their automated systems.
By building these four steps into an automation strategy, IT leaders can develop highly efficient automated environments that are AI-driven but still human-governed.
The message from the world of regulation is clear: if you want your AI-driven automation strategy to be robust and productive in the long-term, it’s not enough to simply trust automation based on how well it tested during the development phase.
Robust governance for the AI era demands that organizations establish centralized oversight, access control, auditability, and accountability as top priorities in their automation strategy, ensuring that even the most intelligently automated systems remain firmly within human control throughout their lifecycle.
Organizations that act now will not only stay compliant with future regulations around AI, but they’ll also gain a competitive advantage by building trust, resilience, and efficiency into their IT operations for years to come.
ScriptRunner provides a centralized Agentic Automation Platform for the Microsoft ecosystem, enabling organizations to securely manage, delegate, and audit automations, including those executed by AI.
With built-in logging, policy enforcement, and traceability, ScriptRunner helps ensure that every automation stays compliant, controlled, and accountable.
See how ScriptRunner supports compliant agentic automation. Start your free trial today.