AI and privacy: What B2B decision-makers need to know

In the Privacy Soapbox, we give privacy professionals, guest writers, and opinionated industry members the stage to share their unique points of view, stories, and insights about data privacy. Authors contribute to these articles in their personal capacity. The views expressed are their own and do not necessarily represent the views of Didomi.

Do you have something to share and want to take over the privacy soapbox? Get in touch at blog@didomi.io

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Every industrial revolution had its spark. 

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The steam engine enabled mechanized production. Then came electricity – factories could operate 24/7, so mass production became a thing. The transistor heralded the age of computers, the digital economy, and all that Moore’s Law talk. 

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And now, AI is here.

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AI is becoming an essential tool for businesses, helping to streamline operations and improve decision-making. Here are a few ways companies are using AI today:

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• Sales Forecasting: AI analyzes historical data, market trends, and competitor activity to improve pipeline predictions – reducing reliance on gut instincts and manual number-crunching.
• Inventory Management: AI-powered demand forecasting helps suppliers prevent stock shortages or overages, improving efficiency.
• Logistics Optimization: Companies like DHL use AI to optimize delivery routes, saving on fuel and labor costs.
• Marketing Personalization: AI enables hyper-targeted campaigns by analyzing customer behavior, improving engagement and conversion rates.

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At its core, AI isn’t replacing business fundamentals – it’s enhancing them, helping companies make better, data-driven decisions.

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The privacy dilemma in AI

“Ultimately, arguing that you don't care about the right to privacy because you have nothing to hide is no different than saying you don't care about free speech because you have nothing to say.”

- Edward Snowden

AI is only as smart as the data it consumes. Every prediction, insight, or decision comes from training on massive datasets. 

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For businesses like yours, that presents a unique problem.

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AI models can process vast amounts of data, from customer purchase histories to employee records, depending on how they are designed and used. Without clear limitations, they may analyze sensitive information, raising concerns about security and misuse.

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The real issue isn't just how AI processes data – but how that data is stored, accessed, and protected. A poorly secured dataset can be a goldmine for hackers, and mismanaged access to AI-driven insights can lead to unintended privacy violations or breaches of trust.

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Remember the Facebook-Cambridge Analytica scandal? AI-driven data analysis turned personal information into political profiling. This led to global backlash and Meta, the parent company of Facebook, ended up paying $725 million (£600 million) to resolve a lawsuit over the breach. The case alleged that Facebook let third parties, including the UK-based firm, access users' personal information without proper consent.

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When exploring AI tools for small businesses, it's crucial to prioritize secure data handling practices to protect sensitive information and maintain customer trust.

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With unauthorized access, you have scenarios where sensitive information – your clients’ names, account numbers, or even social security numbers – gets exposed. And this is bound to hit your bottom line.

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According to IBM, the average cost of a data breach in 2024 was a staggering $4.88 million. That's 10% higher than last year. On the other hand, organizations that extensively used security AI and automation saved an average of $2.22 million compared to those that didn’t.

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Types of sensitive data processed by AI models

Elon Musk once warned about AI, calling it “the biggest existential threat” and likening it to “summoning the demon.” While concerns about superintelligent AI remain speculative, what’s happening today is much more immediate: vast amounts of personal and business data are being processed, stored, and sometimes exposed in ways users may not fully understand.

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Understanding what types of data AI models handle – and the privacy risks associated with them – is key to making informed decisions. Here’s what’s at stake:

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• Personal Identifiable Information (PII): Some AI models process PII, which includes names, addresses, Social Security numbers, and phone numbers, for purposes like identity verification and personalized marketing. However, handling this data comes with risks – if breached, it can lead to identity theft and fraud. It’s also important to note that PII definitions vary by country and even by state. What is considered sensitive in the U.S. under CCPA may differ from how GDPR or other international regulations classify personal data.
  
  
• Financial Data: Some AI-powered systems process financial data, including credit card numbers, bank account details, and payment histories, for purposes like fraud detection, transaction processing, or credit assessments. While these technologies improve efficiency and security, they also introduce risks – if compromised, financial data can lead to unauthorized transactions and identity fraud. For businesses and individuals alike, credit monitoring can serve as an additional layer of protection, helping detect suspicious activity early. If you're wondering whether it's a worthwhile investment, this guide breaks it down.
  
  
• Health Records: AI is revolutionizing healthcare by analyzing patient histories, diagnoses, and treatments. While it leads to better care, breaches can expose deeply personal details. For your clients, it’s not just embarrassing – it can ruin reputations or lead to legal liabilities.
  
  
• Behavioral Data: This includes your customers’ online browsing habits, purchase histories, and app usage patterns. AI uses it to predict preferences and behaviors, driving sales or ad targeting.
  
  
• Biometric Data: We’re talking fingerprints, facial recognition, and voice patterns that AI systems use for authentication and security. Unlike passwords, you can’t reset your face or fingerprints.
   
• Location Data: GPS and location history are often tracked by AI for navigation, targeted ads, or logistics.
  
  
• Corporate Data: AI systems in B2B settings analyze sensitive internal data like trade secrets, client lists, and financial projections. Breaches here don’t just hurt your reputation – they can destroy your competitive edge.
   
• Communications Data: AI-powered tools like chatbots or virtual assistants store interactions, including messages, voice recordings, video recordings. This helps refine services and improve customer experience. 

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While the types of data listed above provide business value, they also introduce significant privacy and security risks. To mitigate these risks, organizations need a comprehensive approach to data protection, including:

• Strong encryption to safeguard sensitive information from unauthorized access.
• Regular security audits to identify vulnerabilities and ensure compliance.
• Compliant consent management practices to ensure data is collected and used transparently.
• Clear internal policies on how AI-driven systems handle personal and financial data.
• Identity theft protection measures to safeguard both customers and employees from fraud.

Without these safeguards in place, businesses face not only data breaches and security incidents but also legal consequences and reputational damage. This brings us to the next challenge: the growing regulatory landscape surrounding AI and data privacy.

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Navigating data privacy regulations and avoiding costly fines

AI took the business landscape by storm, and now the rules are catching up. 

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If you’re operating in the EU, you have the GDPR to deal with. It’s one of the strongest privacy laws out there. Under GDPR, companies must obtain explicit consent before collecting personal data. Violations aren’t cheap. Just ask Google, which faced a $57 million fine in France for unclear data policies.

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In the U.S., the California Consumer Privacy Act (CCPA) – amended by the California Privacy Rights Act (CPRA) – gives Californians the right to know what data businesses collect, how it’s used, and even request its deletion. Non-compliance can result in fines of $2,500 per violation or $7,500 for intentional violations.

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But these are just the beginning.

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Countries worldwide are enacting AI and data privacy laws. Brazil’s LGPD and Japan’s APPI set strict data protection frameworks, while the EU AI Act (effective since August 2024) introduces risk-based classifications, requiring oversight for high-risk applications like hiring and credit scoring.

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In the U.S., privacy laws vary by state – 20 states now have comprehensive privacy regulations, including California, Virginia, and Colorado. AI-specific laws are also emerging, such as Utah’s AI Policy Act and regulations in Illinois and NYC addressing AI bias in hiring.

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With regulations evolving rapidly, businesses must stay informed to ensure compliance and mitigate legal risks.

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So, how does this impact you?

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Compliance isn’t just about avoiding fines. It’s about showing your customers you value their trust. 

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Following these laws helps you:

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• Demonstrate accountability in how you handle sensitive customer data.
• Mitigate risks associated with breaches or unethical data use.
• Build stronger, more transparent relationships with your clients.

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To stay ahead, you need clear policies for data collection, usage, and storage, as well as training your teams to follow privacy best practices. These steps make a big difference in ensuring your AI initiatives align with the law.

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Building trust with ethical AI and data protection

As AI-driven systems become more integrated into business operations, organizations must take proactive steps to ensure ethical data handling and compliance. Building trust with customers and stakeholders requires a comprehensive approach to data privacy, security, and transparency.

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Here’s how businesses can strengthen AI ethics and data protection:

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• Ensure compliant data collection and consent management: Businesses must collect, store, and prove user consent in accordance with applicable laws like GDPR, CCPA/CPRA, and emerging AI regulations. Tools like consent management platforms help streamline compliance.
  
  
• Communicate transparently about AI use: Customers should know what data is being collected, how it’s used, and whether AI models process it. Clearly outlining AI’s role fosters trust and prevents reputational risks.
  
  
• Implement strong data protection measures: Secure sensitive data with encryption, regular audits, and access controls to prevent unauthorized use or breaches. A well-defined cybersecurity framework minimizes vulnerabilities.
  
  
• Prepare for the worst with risk mitigation strategies: Despite strong protections, breaches can still happen. Investing in identity theft protection and cyber insurance can help businesses recover from incidents, covering legal fees, credit monitoring, and PR damage control.

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Taking these steps helps businesses not only stay compliant but also build long-term trust with customers, partners, and regulators.

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Responsible AI scales your business 

AI brings endless opportunities – streamlined processes, better decision-making, and incredible customer insights. But it comes with responsibility. 

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Think about the trust your B2B clients place in you. They share their data, expecting you to protect it. Break that trust, and you risk lawsuits and your reputation going down the drain. 

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That’s why privacy and ethical practices must be at the core of your AI strategy. Tools like consent management systems, proactive training for your teams, and smart safeguards can make a big difference. They show your customers – and the world – that you’re committed to doing things the right way.

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That’s not just good ethics – it’s good business.

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So, what’s next for you? 

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Treat that data like the valuable asset it is and protect it at all costs. Ask yourself the hard questions:

• Do you have clear policies for data collection? 
• Is customer consent fully documented?
• Do you have proper security structures in place?

Adopt tools that simplify compliance. Train your team to handle data ethically. Stay informed about emerging laws and best practices. And always put trust at the center of your decisions.