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As technologies such as Artificial Intelligence continue to advance and our lives are increasingly intertwined with it, ethical, social, security and privacy challenges have also emerged that require regulations and guidelines to try to minimize the risks involved in their implementation.
In addition to regulation, it is key not only to comply with established regulations, but also to adopt ethical and transparent practices in society and companies. AI developers must carefully consider the impact of their decisions on individual and collective rights when creating AI algorithms.
Next, we will present three steps that governments, companies and individuals can and must follow to contribute to the creation of a safe and regulated Artificial Intelligence ecosystem.
Regulation as a first step
Recently the European Union has taken a significant step in becoming the first region in the world to adopt a comprehensive law to regulate AI. Its objective is to ensure that the AI systems used in the EU are safe, transparent, traceable, non-discriminatory and environmentally friendly and that they are supervised by people, rather than automated systems. In addition, the law agrees on a two-tier approach: it requires transparency for all of these general-purpose AI models, and even stricter requirements for “powerful” models.
The European Union's recent initiative is a step in the right direction, but global action is needed to effectively address emerging challenges. While there is unanimity regarding the need for AI regulation, the specific details of how to do it around the world remain undetermined.
The European Union's recent initiative is a step in the right direction, but global action is needed to effectively address emerging challenges. While there is unanimity regarding the need for AI regulation, the specific details of how to do it around the world remain undetermined.
Risk mitigation as a second step
Developers and companies must adopt robust security practices and anonymization and encryption techniques to protect user data and ensure the integrity and confidentiality of information.
Gartner estimates that, by 2026, organizations that establish transparency, trust and security guidelines will see a 50% improvement in their AI models in terms of adoption, business objectives and user acceptance.
The disclosure of sensitive information is one of the main fears when using and continuing to develop AI platforms. The ability of this technology to access sensitive information, such as a person's location, preferences and habits, poses risks of unauthorized dissemination of data.
For example, there is a risk of inadvertently disclosing sensitive data in your responses, leading to unauthorized access to data, privacy violations and security breaches. It is also likely that if the same data that is used to train LLM models can end up exposing personal information, API Keys, Secrets, and more.
Companies that integrate privacy principles from the design phase of AI solutions, commit to local and international regulations, and actively participate in industry initiatives can help establish a solid foundation of trust in this technology, while mitigating potential risks.
Commitment as a third step
In addition to regulation and technical measures, the education and awareness-raising are key to encouraging responsible use of AI. The active commitment of all actors involved in the development and use of AI is essential to create a safe and regulated ecosystem.
The success of generative AI in cybersecurity or any other application depends on collaboration between technology and work teams. The enormous capacity for information analysis and hazard detection enabled by AI must be complemented by interpretation and human decision-making capacity.
As providers of technological solutions, InConcert is constantly committed to the security of its technology, operations and data, meeting the highest standards. That's why it constantly develops, partners and invests in security tools that allow us to be one step ahead in the face of any emerging danger.
For example, When training generative AI, it protects and anonymizes the data used to avoid any type of breach that could generate vulnerabilities against threats and attacks and prevent any accidental or malicious data leak. In addition to having several certifications and adopting best practices to comply with the highest security standards, thus ensuring the integrity and confidentiality of data.
We are certainly at the beginning of this dialogue, but it is now possible to promote regulation, collaboration, and commitment, both in our personal and professional lives. Is it possible that we will ever reach the point of responsibly exploiting the potential benefits of AI, while maintaining strong protection of people's fundamental rights to privacy?
