Artificial Intelligence (AI) is transforming our world in unprecedented ways. It promises to deliver cost savings, a competitive edge, and a foothold in the future of business. As AI development continues at high speed and AI systems become more advanced, the need for important privacy and ethical implications has also been highlighted.
As AI advances societal initiatives, leading to enhanced prosperity and new scientific discoveries, it can also cause biased decisions and lead to privacy violations. Thus, while the rate of AI adoption continues to rise, this growing use of AI has raised many concerns about its impact on user privacy.
According to Info-Tech Research Group’s latest research, ‘Conduct an AI Privacy Risk Assessment’, AI’s reliance on extensive data analysis, often containing personal information, presents a complex ethical and operational challenge amid evolving data privacy laws.
Thus, aligning AI technology with a privacy strategy generates trust among customers and stakeholders in an evolving business environment.
In an era of deepfake videos, voice cloning and cybercrime, safeguarding personal data has become a major business imperative. Globally, data privacy is protected by international and national laws and regulations such as the EU Act, GDPR and CCPA.
The EU AI Act, also known as the EU Artificial Intelligence Act, is the world’s first concrete initiative for regulating Artificial Intelligence, which aims to drive trustworthy AI by laying down harmonised rules governing the development, marketing, and use of AI in the EU. The AI Act aims to ensure that AI systems in the EU are safe and respect fundamental rights and values.
Against this backdrop, organisations should apply strict controls and lay down frameworks to build a privacy-first approach to ensure responsible AI implementation. Organisations can facilitate compliance and build trust in technology implementations by aligning AI initiatives with existing processes and regulatory requirements.
Steps to build a privacy-first approach:
- By identifying high-risk processes, organisations can take steps to evaluate processes where end-user privacy is at risk. By clearly documenting how the system processes PII and whether consent is obtained or not, IT leaders can take steps to safeguard data privacy.
- There are various technical methods to measures deployed by external third parties who may handle data to ensure data integrity against unauthorised access and cyber threats.
- It is important to assess supply chain risks within software or systems in cases where data needs to be transferred to any external entity.
Benefits of implementing data privacy controls
Data privacy is critical for several reasons. These include protecting personal information, retaining trust, upholding ethical standards, and avoiding legal consequences.
Organisations prioritising data privacy foster great trust among customers and other stakeholders. A company can gain respect for fair and ethical practices by ensuring transparency in handling personal data. Moreover, an organisational approach that complies with data privacy laws ensures that a company avoids any legal hassles, non-compliance fines, and damage to the brand’s reputation.
While data privacy always comes with inherent complexities, these issues have been exacerbated after the rise of generative AI, which needs massive amounts of data to sustain its learning momentum. All these factors have made data privacy a prime concern.
As AI keeps advancing exponentially, people must remain vigilant in countering intrinsic challenges and ensuring it is used to promote public good rather than being misused for nefarious activities. This can only happen if individuals and institutions comprehend the sanctity of data privacy. Thereby, one could ensure AI is used ethically, responsibly and transparently for the greater good of humankind.
Data privacy should be fundamental to an organisation’s operations and technologies. Thus, building a privacy-first approach and driving responsible AI implementation is recommended by aligning AI initiatives with existing processes and business objectives.
