CIO OPINION supervised autonomous systems making harmful decisions are high, especially when they have access to sensitive data or wide networks.
Vulnerability to external attacks is a critical point. Hackers can exploit autonomy to cause damage capable of replicating and amplifying its effects. Robust measures, such as the implementation of emergency shutdown systems, advanced cybersecurity and encryption frameworks and the definition of clear operational limits, in addition to continuous governance and rigorous monitoring, are indispensable to protect agents from failures and manipulations.
In the ethical field, concerns about algorithmic biases and lack of transparency are even more evident. Agent systems are capable of perpetuating discriminations present in the training data, while the complexity of their decisions makes it difficult to track the criteria used. The advanced automation brought by Agentic AI can also generate economic and social impacts, such as the unequal displacement of jobs. Thus, adopting principles of impartiality, accountability, transparency and explainability, and conducting constant audits to ensure that decisions are understandable and aligned with social and organizational values, is imperative.
Another major challenge lies in regulatory compliance, such as the General Data Protection Regulation( GDPR) in Europe and the General Data Protection Law( LGPD) in Brazil, which require rigorously processed data and autonomous systems with operations within clear boundaries. The absence of a unified global standard regarding regulation aggravates risks, exposing companies to legal risks, especially in cases of ambiguity about liability for incorrect decisions or damages caused by agents, leading to severe penalties and compromising the company’ s reliability. For this reason, investing in compliance, with recurring external audits, shared responsibility mechanisms, and data management protocols is essential.
Agentic AI relies on robust and reliable data to operate, making it essential to implement data integration,
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