In the current context where digitization is accelerating, Artificial Intelligence (AI) emerges as a catalyst for innovation in companies, projecting to reach a market of 2 million dollars by 2030.
AI, which has been part of the technological arsenal since the 1950s, has seen exponential growth recently thanks to advances in machine learning algorithms and neural networks. These advances allow “intelligent” applications to perform complex analyses and make quick decisions, increasing efficiency and analytical capacity comparable to humans.
To facilitate the understanding of its evolution, the Defense Advanced Research Projects Agency (DARPA) has categorized the development of AI into two “waves”: the first, focused on discovering and recognizing through rule-based systems and machine learning; and the second, which allows systems to “feel and see” through computer vision, enhancing analytical and adaptive capabilities.
However, the implementation of AI is not without risks. López Monje warns of the potential dangers of manipulating AI for unauthorized purposes, which could result in significant vulnerabilities within business infrastructures. These challenges emerge from complex data models and the interdependence that characterizes modern AI, creating a dualism that can be both an asset and a risk.
In this scenario, companies face the challenge of balancing the promise of AI with its potential risks. Best practices include constant monitoring of the lifecycle of AI products to identify vulnerabilities, evaluating the security of the data used in algorithm training, and developing specific strategies to mitigate AI threats.
Furthermore, collaboration with specialized providers in IT services and cybersecurity solutions is crucial to ensure effective understanding and execution of protection measures, allowing companies to leverage the benefits of AI while protecting against vulnerabilities.
In conclusion, while generative AI offers a landscape full of opportunities to improve business performance and discover new revenue streams, it is essential to carefully manage its inherent security risks. With proper guidance and a balanced approach, companies can successfully navigate the complex landscape of modern AI.