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 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.

First time in history we might be prioritizing security over innovativeness, as we should. Microsoft has finally sorted the wheat from the chaff and started to pay deserved attention to security:

“If you’re faced with the tradeoff between security and another priority, your answer is clear: Do security,” Microsoft CEO Satya Nadella said in a memo issued to his employees last month.

“In some cases, this will mean prioritizing security above other things we do, such as releasing new features or providing ongoing support for legacy systems.“

But although supra-national and region-specific regulations on responsible AI and data protection are underway, it’s still incumbent on individual companies to establish a set of risk-related best practices — especially, if they’re looking to derive value from AI-driven innovation.

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, potentially exposing business data to vulnerabilities.

Unclear data residency

An overwhelming majority of generative AI applications offer little oversight of data storage and processing destinations, which may be an inconvenient circumstance if your organization has strict requirements around data residency. Your company’s legal or regulatory obligations might conflict with relevant data privacy laws in your jurisdiction, potentially putting you at risk of hefty fines.

So unless you indicate a specific preference or turn to regionally hosted models, your AI solution places your data in the red zone.

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.

Dubious quality of data sources used to fine-tune the model

‘Garbage in, garbage out’ — this adage holds true even for the most advanced AI models. Poor quality of the source data used for fine-tuning can trigger inaccurate outputs. In most cases, there’s little a company can do to head off this pitfall since organizations have limited control over the origin and quality of data used by vendors during fine-tuning.

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.

Also, the General Data Protection Regulation, California Consumer Privacy Act, and other bodies set a very high bar, unreachable for most AI models, when it comes to the effectiveness of anonymization.

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.

Front-page data breaches involving AI: lessons learned?

If there’s one thing we can learn from the tech news is that even high-profile companies fail to effectively protect user data. And with AI technologies, this mission becomes even more formidable due to the expanded vulnerability surface.

Microsoft’s massive data exposure incident that took place in 2023 is one of the many stark reminders, highlighting AI and data privacy concerns. In this incident, Microsoft’s AI research team accidentally exposed 38 terabytes of data from employee workstations. As a result, a wide range of highly sensitive information slipped through the cracks, including personal computer backups, passwords, secret keys, and other data. Consequently, the attacker gained complete control over the system, including the ability to delete and manipulate existing files at will.

Foundational model owners aren’t immune to data bridges either. Recently, OpenAI faced scrutiny after its ChatGPT model made payment-related and other personal information of 1.2% of users visible to some users. This incident underscored concerns from industry experts who have previously criticized OpenAI’s insufficient data security practices.

Undoubtedly, every A-list company that has been exposed to any kind of data breach was quick to take crucial post-breach responses — by patching, adjusting cloud configurations, or taking their applications offline. But considering the ever-high cost of data breaches, no measure is more effective than proactive prevention.

Tech players like Accenture, AWS, and IBM take prevention to a whole new level by shoring up capabilities and processes for responsible AI development and use. While specific points in their blueprints may differ, a common thread runs through their strategies — an unwavering commitment to compliance, data privacy, and cybersecurity.

Navigating legal waters: AI regulatory rules of thumb

Not all AI products are inherently flawed, some popular solutions like Simplifai, Glean, and Hippocratic AI demonstrate that success can be achieved while meeting privacy regulations and paying due diligence to privacy protection. But we get it: the regulatory landscape is changing fast, making AI development an uncharted territory for first-time technology adopters.

The good news is there are key principles that can drift your development efforts in the right direction and save you a lot of headaches down the road.

Reducing data usage to the essential minimum

First and foremost, you can get a lion’s share of AI data privacy concerns out of the way by keeping the amount of training and operating data to the necessary minimum from the get-go.

There’s a lot you can do to achieve minimal data usage in AI applications:

• Give your data a good scrub — clean and filter the data to get rid of duplicated input, structural errors, and noisy information before training the model.
• Double down on the most relevant variables — leverage feature engineering and distill the most useful patterns from the data.
• Piggyback pre-trained models with larger datasets — turn to transfer learning to train your data on a smaller, specialized dataset.
• Artificially create new data points — use data augmentation techniques to increase the training dataset without collecting additional input.

Providing understandable explanations of how AI systems function and make decisions

A bad reputation associated with the lack of data privacy demonstrated by AI can be partly attributed to the black-box nature of the latter. Safeguarding data becomes a tall order when there’s little explainability in the decisions of machine learning algorithms and deep learning techniques. That’s why building a transparent and explainable system with clear underlying mechanisms and decision-making processes is crucial for an organization to build trust and confidence in emerging technologies.

Making your AI systems explainable boils down to the main three features, including prediction accuracy, traceability, and decision understanding.

Incorporating human review mechanisms to oversee AI decisions

Different regulations, the GDPR and EU AI Act in particular, set out certain obligations for human intervention or human oversight as a means of preventing decision-making based solely on machine intelligence. To meet the requirement, organizations should employ robust review practices to avoid perpetual biases.

There are four main ways to put a rein on the outputs of the smart solution. The most common one is a human-in-the-loop system that is often used in high-risk applications. In this case, a human reviewer is directly involved in the decision-making process alongside AI algorithms. Organizations can also apply post-hoc reviews and exception-handling rules, to promote more accurate output and make sure the system doesn’t disclose any personal data.

Identifying and understanding different risk levels associated with AI systems

Just as the old saying goes ‘forewarned is forearmed’, knowing the risks and possible doomsday scenarios beforehand allows companies to devise effective mitigation strategies.

While there is no one-size-fits-all for conducting a risk assessment for artificial intelligence tools, most frameworks require companies to assign a category of risk to the system and draw up a risk mitigation strategy based on the risk profile.

Ongoing monitoring and system refinement are other non-negotiables of a holistic risk assessment framework that can give you a heads-up about any emerging risks.

Paying special attention to profiling workloads

Some AI applications like facial recognition software or customer services chatbots scan personal user data to create audience profiles based on user’s behavior, preferences, and other criteria. As this exercise involves processing large amounts of personal information, US companies must make sure their profiling is conducted in line with the GDPR, CCPA, PDP Bill, or any other relevant regulation.

In reality, though, users might not be in the know about their data being used for profiling purposes, which is a hard no for ethical and responsible AI use. Also, profiling datasets may become an easy target for hackers, especially if the system has rickety security controls.

Robust safeguards such as anonymization or pseudonymisation as well as technical and organizational security controls are among the go-to safe nets when it comes to shielding profiling data. Also, transparency around profiling methods and data controls in place is important to alleviate users’ concerns.

Ensuring AI systems operate reliably and do not pose risks to users or the environment

According to ISO 42001:2023, AI systems must behave safely under any circumstances without putting human life, health, property, or the environment at risk. To meet these requirements, smart systems shouldn’t operate in silos — they must be weaved into a broader ethical framework that prevents biases in decision-making and mitigates environmental footprint stemming from resource-intensive model training.

Proactive risk management coupled with the explainability of algorithms and traceability in your workloads empowers organizations to shore up capabilities and safeguards instrumental to building trustworthy AI systems that benefit society as a whole.

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. Just like vulnerability management, effective security requires continuous attention to three components of an AI system:

• Input security — check applicable data privacy regulations, validate data residency, and establish Privacy Impact Assessments (PIA) or similar processes for each use of regulated data.
• Model security — make sure you have clear user consent or another reason allowed by law to process data. You can use the PIA framework to evaluate the privacy risks associated with your AI model.
• Output security — revisit the regulations to see whether the regulated data is available for secondary processing. Your AI system should also have a way to erase data on request.

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.
• Documented data provenance — create verifiable mechanisms to confirm the origin and history of all data used by the models, especially inference data used for model training. Make sure data lineage and data access in non-production and development regions are in check to stave off data manipulation.
• 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.

And by no means, do not use data directly as input for commercial pre-trained gen AI models, unless you intend to put sensitive information into the limelight.

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.

Responsible AI development demands from organizations a holistic risk management and data privacy approach, paired with a mix of risk-specific controls. By keeping privacy and ethics in AI development and deployment top of mind, you can enjoy the benefits of AI while prioritizing data privacy and promoting trust and accountability in the use of AI technologies.

Originally published on instinctools.com