AI Privacy Concerns: Profiling Through the Risks and Finding Solutions

Today, artificial intelligence is billed as a superpower that brings about unprecedented technological advancements in virtually every industry — and rightly so. But with this incredible progress comes a growing concern: is AI infringing on our privacy?

AI has captured the world's imagination, promising to revolutionize industries and reshape society. Yet, beneath the surface of this excitement lies a growing sense of unease. Concerns about job displacement, consumer exploitation, and the potential for malicious use have tempered initial enthusiasm. The rapid pace of AI development has outstripped our ability to fully comprehend its implications, leading to a critical need for thoughtful examination and robust regulation. AI privacy concerns have been the subject of many debates and news headlines lately, with one clear takeaway: protecting consumer privacy in AI solutions must be a top business priority.

Is your privacy governance ready for AI? Let’s find out.

A pulse check on AI and privacy in 2024

The heady growth of generative AI tools has revived concerns about the security of AI technology. The data chills it triggers have been long plaguing AI adopters — except they’re now exacerbated by unique gen AI capabilities.

Inaccuracy, cybersecurity problems, intellectual property infringement, and lack of explainability are some of the most common generative AI privacy concerns that refrain 50% of organizations from scaling gen AI responsibly.

The worldwide community is echoing a security-focused approach of AI leading players, with a sweeping set of new regulations and acts advocating for more responsible AI development. These global efforts are initiated by actors ranging from the European Commission to the Organization for Economic Co-operation and Development to consortia like the Global Partnership on AI.

The dark side of AI: how can it jeopardize your organization’s data security?

No matter what type of AI solutions you are integrating into your business, prebuilt AI applications or self-built ones, the adoption of AI systems demands a heightened level of vigilance. When left unattended, AI-related privacy risks can metastasize, potentially causing a range of dire consequences, including regulatory fines, algorithmic bias, and other pitfalls.

Lack of control over what happens to the input data or who has access to it

Once an organization’s data enters the gen AI intelligence stream, it becomes extremely difficult to pinpoint how it is used and secured due to unclear ownership and access rights. Along with black box issues, reliance on third-party AI vendors places companies at the mercy of external data security practices that may not always live up to the company’s standards.

Reuse of your data for training the vendor’s model

When a company signs up for a vendor-owned AI system, they unknowingly consent to a hidden curriculum. Most third-party models collect data and reuse it to train vendor’s foundational models, not just your specific use case. This may raise significant privacy concerns associated with sensitive data. Data reuse also works in reverse, introducing biases into your model’s output.

Personally Identifiable Information (PII) violations

You might think that data anonymization techniques place PII under wraps. In reality, even anonymized and scrubbed of all identifiers data can be effectively re-identified by AI based on users’ behavioral patterns. Not to mention, that some smart models struggle to anonymize information properly, leading to privacy violations and serious repercussions for organizations.

Security in AI supply chains

Any AI infrastructure is a complex puzzle consisting of hardware, data sources, and the model itself. Ensuring all-around data privacy demands the vendor or the company to introduce safeguards and privacy protections into all components as any breach in the AI supply chain can have a far-flung effect on the entire ecosystem, including poisoned training data, biases, or derailed AI applications.

6 practices to wipe out AI data privacy concerns

While some companies grapple with AI risk management, 68% of high performers address gen-AI-related concerns head-on by locking risk management best practices into their AI strategies.

Standards and regulations provide a strong ground zero for data privacy in smart systems, but putting foundational principles in action also requires practical strategies. Below, our AI team has curated six battle-tested practices to effectively manage AI and privacy concerns.?

1. Establish AI vulnerability management strategy

Just like any tech solution, an AI tool can have technology-specific vulnerabilities that spawn biases, trigger security branches, and reveal sensitive data to the prying eyes. To prevent this havoc, you need a cyclical, comprehensive vulnerability management process in place that focuses on the three core components of any AI system, including its inputs, model, and outputs.

• Input vulnerability management — by validating the input and implementing granular data access controls, you can minimize the risk of the input vulnerability.?
• Model vulnerability management — threat modeling will help you harden your model by mitigating known documented threats. If you have commercial generative AI models in your infrastructure, make sure to perform close inspection of data sources, terms of use, and third-party libraries to prevent bias and vulnerabilities from permeating your systems.
• Output vulnerability management — strip the output of sensitive data or hidden code to make sure nobody can infer sensitive information and to mitigate cross-site vulnerabilities.

2. Take a hard stance on AI security governance

Along with vulnerability management, you need a secure foundation for your AI workloads, rooted in the wraparound security governance practices. Thus, your security policies, standards, and roles shouldn’t be confined to proprietary models but also extend to commercial and open-source models.

Water-tight security starts with a strong AI environment, amplified with encryption, multi-factor authentication, and alignment to best industry frameworks such as NIST AI RMF.?

3. Build in a threat detection program

To defend your AI set-up against cyber attacks, you should apply a three-sided threat detection and mitigation strategy that addresses potential data threats, model weaknesses, and involuntary data leaks in the model’s outputs. Such practices as data sanitization, threat modeling, and automated security testing will help your AI team to pinpoint and neutralize potential security threats or unexpected behaviors in AI workloads.

4. Secure the infrastructure behind AI

Manual security practices might do the trick for small environments, but complex and ever-evolving AI workloads demand an MLOps approach. The latter provides a baseline and tools to automate security tasks, usher in best practices, and continuously improve the security posture of AI workloads.

Among other things, MLOps helps companies integrate a holistic API security management framework that solidifies authentication and authorization practices, input validation, and monitoring. You can also design MLOps workflows to encrypt data transfers between different parts of the AI system across networks and servers. Using CI/CD pipelines, you can securely transfer your data between development, testing, and production environments.

5. Keep your AI data safe and secure

Data that powers your machine learning models and algorithms is susceptible to a broader range of attacks and security breaches. That’s why end-to-end data protection is a critical priority that should be implemented throughout the entire AI development process — from initial data collection to model training and deployment.

Here are some of the data safeguarding techniques you can leverage for your AI projects:

• Data tokenization — protect sensitive data by replacing it with non-sensitive data tokens as surrogates for the actual information.?
• Holistic data security — make sure you secure all data used for AI development, including at-rest, in-transit and in-use data.
• Loss prevention — apply data loss prevention (DLP) techniques to prevent sensitive or confidential data from being lost, stolen, or leaked outside the perimeter.
• Security level assessment — continuously monitor the sensitivity of your model’s outputs and take corrective actions if the sensitivity level increases. Extra vigilance won’t hurt when using new input datasets for training or inference.?

6. Emphasize security during AI software development lifecycle?

Last but not least, your ML consulting and development team should create a safe, controllable engineering environment, complete with secure model storage, data auditability, and limited access to model and data backups.?

Security scans should be integrated into data and model pipelines throughout the entire process, from data pre-processing to model deployment. Model developers should also run prompt testing locally in their environment and also in the CI/CD pipelines to assess how the model responds to different user inputs and nip potential biases or unintended behavior in the bud.

Balancing innovation and privacy

To remain top of the game amidst the growing competition, companies in nearly every industry are venturing into AI development to tap its innovative potential. But with great power comes great responsibility. As they pioneer AI-driven innovation, organizations must also address the evolving risks associated with AI’s rapid development.?

In Dedicatted , we understand the importance of balancing innovation, data privacy, and ethical considerations in AI development lies in ensuring sustainable technological progress while safeguarding individual rights and societal norms. Compliance fosters transparency and accountability in AI operations, leading to more reliable technology.

Contact us to create more responsible and user-centric AI solutions that are viable in a global market.?