Security and compliance in intelligent automation: the new strategic shield
Security and compliance in intelligent automationhave become a priority for risk committees, driven by the widespread adoption of generative AI. (Only 1.6% of firms have fully integrated AIinto their compliance processes, even though more than 50% plan to increase automation in GRC.) This gap requires a rethinking of control models, continuous monitoring, and responsible governance.
Intelligent automation under regulatory pressure
The expansion of intelligent automationin critical processes requires combining cybersecurity, privacy, and data governance within a single control framework. New regulations on high-risk AI, explainability requirements, and documentation obligations raise the bar. Organizations that treat compliance as a mere formality will face greater security breaches and more complex audits.
From fragmented controls to continuous, data-driven compliance
Regulators expect continuous monitoring tests, not just written policies.
Intelligent automation and advanced analytics platforms enable real-time risk mapping and prioritization.
Recording algorithmic decisions with immutable logs, versioning, and traceability facilitates audits.
Tracking evidence includes metrics, model explanations, and drift alerts for rapid response.
Design workflows that automate compliance evidence and are auditable and aligned with ISO, NIST, and the AI Act.
Governance, culture, and secure design from the outset
The adoption of AI requires strengthening governance and accountability. This requires organized frameworks that monitor risks, compliance, and ethical impacts from design to operation.
Create joint committees (business, legal, security, technology) to review models, bias metrics, supported use cases, and coordinate audits.
Establish ongoing training to detect Shadow AI, reporting channels, and protocols for documenting authorized exceptions.
Require ethical reviews and impact assessments (privacy, discrimination, security) prior to any deployment and maintain decision logs.
Apply security by design to data, algorithms, and supply chains: access controls, traceability, and third-party management.
In this context, security and compliance in intelligent automationgo from being a defensive cost to a strategic advantage: they facilitate innovation with less risk, better negotiations with partners, and greater regulatory confidence. To move forward, it is advisable to assess maturity, prioritize critical use cases, and define a roadmap for AIand automation governance. To design and execute this framework in a robust manner, you can request specialized support from Digital Robots.
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