The role of AI in data protection and compliance

The rapid rate of innovation in our digital age continues to help businesses deliver seamless, personalised customer experiences. Modern consumers have come to demand customised interactions from brands at every touchpoint of their journey.

But in order to provide these tailored experiences, businesses need to gather as much user data as possible to understand each customer’s preferences based on their online interactions. At the same time, customers expect businesses to safeguard their personal data and respect their online privacy. A bit of a dilemma.

The challenge of balancing personalisation and privacy in the digital age is a big one – but not impossible. Thanks to AI, businesses can enhance security to protect users’ privacy.

The role of AI in enhancing data protection and compliance

AI is already known for its ability to gather, store, and sort large amounts of data extremely quickly, allowing businesses to have centralised ownership over their user and business data. There are many ways businesses can safeguard this data, such as security programs and encryption.

But cybercriminals are always searching for ways to penetrate security firewalls to steal valuable data. AI-powered anomaly detection and intrusion protection are great ways for businesses to safeguard data. By monitoring incoming and outgoing network traffic, these applications are able to flag any suspicious activity and violations of security policies.

Automating privacy policy enforcement means that an organisation’s compliance initiatives are constantly enforced and transparent from a centralised policy repository. Powered by AI, this process is entirely automated throughout an entire organisation, helping to consistently reach compliance benchmarks while also ensuring data safety.

Privacy-preserving machine learning (PPML) is another technique used by organisations to protect user and business data from nefarious threats. PPML is a process that prevents data leaks in machine learning algorithms by blending multiple privacy-enhancing strategy input sources to train ML models without exposing the private data from the original source.

The impact of data protection regulations on AI adoption

Emerging global data privacy standards are forcing data-driven businesses to adopt AI to help them achieve compliance. 

Europe’s General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), together with growing public concerns around general data collection, have left organisations with no choice but to leverage the power of AI to ensure their user data is protected and compliant.

But these compliancy initiatives make it harder for businesses to gather, store, and use data – data that is used to create personalised user experiences. The more difficult it is to collect valuable data, the more expensive it will be. This will have an impact on AI adoption, especially from smaller businesses that are trying to build AI systems but can’t access large streams of first-party data.

Furthermore, compliance standards such as the GDPR reduce the efficacy of AI as it requires personal consent from each user when it comes to the use of credit card applications and e-recruiting. Alternatively, the need for AI increases when it comes to cyber detection and cybersecurity, spurring investment into AI cybersecurity. 

Best practices for managing privacy risks in AI implementation

Here are a few ways for data-powered organisations to manage their privacy risks when implementing AI into business systems.

Data minimisation

To reduce risk, it is a good idea to collect only the minimum amount of personal data needed to deliver personalisation. This means only using the data you need and deleting the rest – or simply don’t gather irrelevant data in the first place. The less data you have, the less risk you take on.

Anonymisation

To ensure the utmost level of personal data security, organisations are opting for data anonymization. It is a data processing technique that edits or deletes any information that can be used to personally identify someone. The result is anonymized data that is impossible to link to any single individual.

Transparency and user content

Many users are not entirely comfortable with their data being stored and used, but almost all users are certainly unhappy when their data is used without their consent. A lack of transparency in this regard is detrimental to a business. Therefore, it is crucial that businesses operate with complete transparency regarding how their user data is collected and used. The more open and honest you are, the more you will be able to establish user trust.

Continuous monitoring and auditing

The only way to ensure your user and business data is safe at all times is if you continuously monitor and audit your data. This helps keep your teams on top of any issues when they occur and gives you enough time to make corrections before they become large problems. This also instils trust in customers and stakeholders who are rest assured knowing their data is continuously monitored and safeguarded against threats.

Summary

There is no way around it – AI is crucial to the success of any modern, data-powered enterprise. The vast array of benefits in productivity and cost reduction makes it a necessary component in the digital era.

But in order for it to thrive and yield the results that can take any startup to the top, it means establishing a transparent and trustworthy relationship with user data.

Organisations can still leverage AI to provide value-added personalisation at every touchpoint along the user journey while also still being compliant with global data privacy standards. The trick is understanding the requirements of both and leveraging the cons to satisfy customers and legal entities in a transparent manner.

Mario Grunitz

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