AI has already brought businesses many advantages, including increased efficiency and a greater understanding of what they can achieve. But it is certainly not without its challenges, many of which involve data security and protecting privacy.
With the increased use of AI systems in normal day-to-day operations, users must find reliable solutions to protect their data or privacy. Here are some tips from a cybersecurity expert, Adam McManus of Etobicoke, Ontario.
1. Beware of AI Risks
AI works by analyzing large amounts of data such as health records, bank details, and behavioral patterns from which it learns and evolves to make tasks more efficient.
The challenge with AI is that it can inadvertently reveal sensitive data. For example, certain AI algorithms may expose personal information (e.g., gender) in their output, having been trained on unsecured data. Moreover, cybercriminals could target security flaws in AI systems to steal data and manipulate models, undermining privacy.
2. Enforce Data Encryption
Data encryption remains one of the best ways to protect data, especially when it comes to AI systems. This means that information is encrypted while stored and sent, so even if someone unauthorized gains access, they cannot easily read or misuse the content. To strengthen the security of data feeding into AI models, both cloud providers and companies should use powerful encryption standards. Similarly, data created or stored by AI systems will need to be safeguarded from malicious events.
3. Anonymize or Mask Data for More Privacy
Before feeding personal data into AI systems, it is important to ensure it is anonymized and masked to a higher degree. Data anonymization protects privacy because data can no longer be traced back to a person through AI processing. Specifically, methods such as differential privacy can provide powerful solutions in these cases.
4. Reduce the Amount of Data Collected and Shared
Practicing data minimization means collecting the minimum amount necessary for a particular AI application. Data over-collection means companies typically store more sensitive information than necessary, expanding the potential risk of a breach. AI users must also be careful about what data they share, as third-party vendors or cloud services may not adhere to equivalent security or privacy standards.
5. Foster Secure AI Models
Security must be a first-class citizen in the design of any AI system. This involves using privacy-aware and secure machine learning models with native defenses against adversarial attacks, data poisoning, or model theft. AI explainability is also becoming increasingly important, as it ensures AI decisions can be interpreted and understood, making it easier to detect when something has gone wrong, like a security breach.
6. Continuously Monitor and Update AI Systems
AI systems never stay the same, and threats evolve over time. Monitoring AI performance is essential to detect deviations, suspicious incidents, or breaches. Keeping track of how data is being used throughout AI models helps detect potential risks and eliminate them before escalation. Models also need regular updates, such as security patches, to maintain resilience against new cyber threats.
7. Empower Users with Transparency
One of the biggest complaints about AI today is the lack of transparency about how data is being used. The more we know about AI systems’ workings, the easier it will be to trust them. Ensuring transparency and clear consent mechanisms and allowing users to delete their data from systems can build trust and protect privacy.
8. Adopt Privacy Regulations
With AI becoming part of daily life, regulations like the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the U.S. are crucial for a future filled with technological achievements. These laws ensure businesses protect consumer data, maintain transparency, and give individuals more control over their personal information. Complying with these regulations also benefits businesses by promoting good data quality practices.
As AI continues to transform industries and everyday life, data protection should be a higher priority than ever, according to Adam McManus. Balancing privacy-conscious living with AI’s conveniences is challenging, requiring proactive steps to prioritize security in AI development and usage.
