COMMENTARY: It starts with a small win. A business analyst builds a simple AI workflow automation in the UiPath agentic testing tool to reconcile invoices overnight. It works flawlessly, so the finance department quickly builds more.Soon, dozens of automations are running across departments, moving data between applications, handling customer workflows, even accessing sensitive financial records.[SC Media Perspectives columns are written by a trusted community of SC Media cybersecurity subject matter experts. Read more Perspectives here.]Under the hood, these automated workflows create an exploitable path to sensitive data. Since they process text programmatically, attackers can tamper with that content to embed a malicious query that the automation runs as legitimate logic.As AI automation becomes a standard tool in the enterprise, it's often growing much too fast, without controls to govern it. What begins as a productivity boost can quickly turn into unmanaged risks.Soon, organizations accumulate a backlog of “security debt” in their automation ecosystems. Misconfigured permissions, vulnerabilities, outdated connectors, and undocumented dependencies pile up, creating fertile ground for breaches or compliance violations.Agentic AI won’t just add more automations: it will multiply the risks already associated with them. By putting governance and oversight in place now, enterprises can ensure trust, while preventing security debt from silently accumulating into a long-term liability.Yair Finzi, co-founder and CEO, Nokod SecuritySC Media Perspectives columns are written by a trusted community of SC Media cybersecurity subject matter experts. Each contribution has a goal of bringing a unique voice to important cybersecurity topics. Content strives to be of the highest quality, objective and non-commercial.
Citizen developed tools lack guardrails
AI automation thrives because it empowers employees to automate repetitive work. But when Citizen Developers build automations outside traditional IT oversight, they introduce new vulnerabilities that include:- Limited visibility: Security teams often lack a real-time inventory of automations, their configurations, and the data they touch.
- Data exposure: Automations frequently handle sensitive information like financial transactions or customer data, amplifying the stakes of any misconfiguration.
- Compliance concerns: Without consistent governance, meeting regulations such as GDPR, HIPAA, or PCI DSS becomes nearly impossible.
- Resource constraints: Security and compliance teams do not have the bandwidth to manually review hundreds of automations, while business users are not trained to code securely.
Legacy controls fall short
Standard application security testing tools are not designed for AI developed, no-code environments because they cannot parse automation code or identify risks hidden in bot logic. As a result, many enterprises can’t detect or enforce policies on bots that support critical business functions.Take a large enterprise that automates customer refund workflows. The automations access payment gateways, ERP systems, and email platforms. Traditional perimeter defenses cannot see whether an automation stores session tokens in plain text, or if it’s calling an outdated third-party component. The risk lives inside the automation logic itself, invisible to conventional appsec tools.Regulators have paid attention. Automations often skirt traditional audit trails because they act in the background, executing tasks with human-like permissions but without the accountability of human identity systems. When auditors ask who accessed sensitive data, the answer may be a bot account shared by multiple users. This lack of lineage can undermine compliance with GDPR, HIPAA, SOX, or industry frameworks like PCI DSS.Imagine an audit where an AI automation has been moving health records between databases. Without proper tagging, logging, and controls, the organization cannot prove compliance with data protection rules.Balance security with innovation
Simply shutting down citizen-developer AI automation projects isn’t an option. Teams need to enforce clearly defined guardrails and governance without crimping innovation. By embedding controls into the automation lifecycle itself, security teams can scale alongside business users instead of becoming bottlenecks.Without visibility and governance, misconfigurations and unchecked automations accumulate into systemic vulnerabilities. When auditors arrive, the lack of traceability across bots results in a compliance gap. The remediation effort consumes weeks of staff time and diverts focus from strategic projects.We can’t implement AI automations with a set-and-forget mentality; they require the same rigor applied to human identities and enterprise applications. In other words, they require continuous monitoring and policy enforcement.Blueprint for secure AI automations
To reduce security debt, contain risk, and maintain compliance, enterprises should consider the following practices:- Inventory and classify all automations: Maintain a live catalog of automations, their owners, environments, and data touchpoints to map automations, users, environments, and connectors.
- Enforce least privilege access: Scope automations only to the systems and data they need, revoking unneeded privileges.
- Automate compliance tagging: Apply metadata to sensitive data to ensure audit readiness.
- Integrate security into development: Embed security checks into automation build and deployment pipelines, which offers citizen developers actionable remediation guidance.
- Monitor continuously: Use automated monitoring to detect misconfigurations, vulnerabilities, risky behaviors, and outdated components.
- Establish governance policies: Define and enforce enterprise-wide standards for design, data handling, and identity management of automation development.
