AI Data Security and Privacy for SMEs: 2025 Guide

Introduction

Artificial intelligence (AI) has become a strategic ally for small and medium-sized enterprises (SMEs), driving productivity and innovation. Tools like virtual assistants, chatbots, or predictive analytics systems are now within reach of businesses of all sizes. However, along with these advantages come critical concerns about data security and privacy.

This is no minor issue: according to IBM, 13% of organizations surveyed have already experienced a security breach in AI models or applications, and an additional 8% weren’t even sure if they had been compromised. Even more alarming, 97% of affected companies lacked specific access controls for their AI systems, demonstrating that many businesses adopt AI without basic protections.

For SMEs, which typically have more limited cybersecurity resources, a breach can be especially devastating both in financial cost and loss of customer trust.

Why Should Your SME Care About This?

Because data privacy and security are not optional in 2025: they are a fundamental requirement for doing business. The average cost of a data breach globally is now around $4.44 million, a figure that can put any company at risk.

And it’s not just about fines or financial losses; your reputation and customer trust are also at stake. SMEs handle sensitive data (such as customer information, payment details, intellectual property) and when incorporating AI into their processes, they must ensure that this information remains protected.

In this practical guide we’ll explore:

• The main security risks associated with using AI in business
• The current legal framework (GDPR, CCPA)
• Best practices and checklists for using AI safely
• Real error cases to extract lessons learned
• Answers to frequently asked questions

The goal is clear: for your business to benefit from AI without compromising the privacy of your data or the security of your operations.

Main Security Risks When Using AI in Business

Adopting AI brings advantages, but it also introduces new risk vectors that SMEs must understand. Here are the main security and privacy risks:

1. Confidential Data Leaks

One of the most immediate dangers is the potential exposure of sensitive information through AI tools. For example, if company employees enter private data (such as customer details, source code, or internal documents) into an external AI service without proper precautions, that data could be stored or even used to train models.

This happened in 2023 when Samsung employees entered confidential code into ChatGPT for help, not realizing that the information was being sent to external servers.

A recent report revealed that 26% of organizations detected that more than 30% of the data their employees enter into public AI tools is private or sensitive data.

2. Privacy and Regulatory Non-Compliance

Improper use of AI can lead to violating data protection laws like GDPR. For example, entering customers’ personal data into an AI tool without their explicit consent could constitute an infringement.

In Spain, the AEPD (Spanish Data Protection Agency) fined companies more than 13 million euros in total for data breaches in 2024, with breach sanctions representing 37% of total fines that year.

3. AI-Powered Cyber Attacks

Criminals are also leveraging AI to improve their attacks. A clear example is deepfakes and automated identity impersonation.

In 2025, cases have become common where attackers create highly realistic fake videos or audio (for example, imitating an executive’s voice or face) to deceive employees and obtain access or fraudulent payments. There has been a dramatic increase in phishing email volume since the emergence of tools like ChatGPT, as attackers use them to generate more personalized messages.

4. Specific Vulnerabilities in AI Models

AI itself can be the target of new types of attacks:

• Data poisoning: A malicious actor introduces manipulated data into training to alter results
• Prompt injection: Techniques that exploit language model instructions to reveal private information
• Jailbreak exploits: Attempts to make AI execute unauthorized actions

In 2025, a zero-click vulnerability was documented in Microsoft 365 Copilot where a simple email with malicious content was enough to extract data without the user clicking anything.

5. Intellectual Property Exposure

If an SME uses third-party AI services, it must understand what happens to the data it inputs. Some cloud AI platforms initially stored user conversations to continue training their models.

Samsung internally banned ChatGPT in 2023 after discovering that employees had inadvertently leaked confidential code. The lesson is clear: without policies and precautions, you could be “giving away” your trade secrets.

6. “Shadow AI” Out of Control

Shadow AI refers to those unofficial uses of AI within the company that IT or management haven’t approved or fully know about. More than 90% of small businesses don’t monitor what their AI systems do once deployed.

This creates a perfect breeding ground for breaches: data can escape without anyone knowing.

Table: Risks and How to Mitigate Them

AI Risk	How to Mitigate It
Confidential data leaks	Encrypt sensitive data; anonymize data before using in AI; policies prohibiting entering critical information in unapproved services
Shadow AI (unauthorized AI)	Clear policies on allowed tools; staff training; DLP systems that detect unapproved AI usage
AI-powered phishing attacks	Cybersecurity awareness campaigns; verification procedures for sensitive requests; anti-phishing solutions
GDPR non-compliance	Data Protection Impact Assessment (DPIA) before implementing AI; legal advice; document compliance measures
Model manipulation	Continuous monitoring of results; keep AI updated; validate training data; filters to prevent revealing sensitive data

Legal Framework: GDPR, CCPA, and Applicable Regulations

The legal framework on data protection fully applies to the use of AI in businesses. SMEs must know and comply with these regulations:

GDPR (General Data Protection Regulation)

This is the European regulation on personal data protection, in force since 2018, mandatory for any company that processes personal data of EU citizens.

Key requirements:

• Limit data collection to what’s necessary
• Have a legal basis for processing (consent, contract, legitimate interest)
• Inform data subjects transparently
• Respect rights such as access, rectification, erasure, and objection

In the context of AI, if you use AI to process people’s data (an algorithm analyzing customer habits, or a chatbot recording conversations), you must ensure all such processing complies with GDPR.

Violating GDPR can result in fines of up to 20 million euros or 4% of annual turnover.

CCPA – California Consumer Privacy Act

This may apply if you have customers or users in California, or if you use AI services from companies subject to CCPA. For tech SMEs with global ambitions, complying with GDPR usually puts you in a good position to also comply with CCPA.

EU AI Act

This is a regulation pending final approval that will specifically regulate AI systems according to their risk level:

• Unacceptable risk: Prohibited (subliminal manipulation, social scoring)
• High risk: Strict requirements (AI in HR, finance, healthcare)
• Limited and minimal risk: Fewer obligations

If an SME uses or develops AI in critical areas, it will need to perform conformity assessments. Starting to comply with these principles now will be a competitive advantage.

Other Relevant Regulations

• Healthcare sector: Medical confidentiality rules, HIPAA (in the US)
• Financial sector: Regulatory guidelines for algorithms
• Marketing: Various digital services laws

Voluntary standards: The NIST AI Risk Management Framework offers useful guides, though only 14% of SMEs know about it.

7 Best Security Practices for AI in SMEs

Here are seven concrete best practices to strengthen security and privacy when implementing AI:

1. Classify and Minimize the Data You Use in AI

Not all data should feed AI systems. Identify what data is truly necessary and avoid including personal or confidential information if it’s not essential.

Minimization principle: The less sensitive data you use, the smaller the impact if a leak occurs. For example, if you’re training a model for marketing, you might be able to use aggregated or anonymous data instead of individual data.

2. Apply “Privacy by Design” and Conduct Impact Assessments

Incorporate privacy and security from the start of any AI project. Conduct a Data Protection Impact Assessment (DPIA) before deploying the tool.

In this assessment you’ll analyze:

• What can go wrong (breaches, biases, misuse)
• What measures you’ll take to prevent it
• How the AI collects and processes data
• What safeguards you’ll implement

3. Choose Reliable AI Providers and Demand Guarantees

If you’re going to use third-party AI services:

• Choose providers with good cybersecurity reputation
• Require data encryption and robust authentication
• Request a Data Processing Agreement (DPA)
• Check for certifications like ISO 27001 or SOC 2
• Ask: Do they store content? Do they use it to train models?

Consider enterprise versions (ChatGPT Enterprise, for example) that guarantee your data won’t be used to train models.

4. Encrypt Your Data and Protect Access

Essential measures:

• Encrypt sensitive data before sending to AI platforms
• Encryption at rest and in transit (HTTPS/TLS)
• Manage credentials and API keys securely
• Use two-factor authentication
• Apply the least privilege principle

5. Define Clear Policies and Train Your Team

People are the first line of defense. Establish AI usage policies:

• What types of data can (or cannot) be entered into AI tools
• Which applications are approved for internal use
• Consequences of violating these rules

Invest in training: Conduct workshops, share real cases like Samsung and ChatGPT to build awareness.

6. Monitor AI Usage and Maintain Control

Don’t leave your AI systems running on “autopilot”:

• Implement continuous monitoring mechanisms
• Review activity logs regularly
• Set up automatic alerts for unusual access
• Plan periodic audits (quarterly)

Only 17% of organizations have technical controls to block access to unauthorized AI tools.

7. Prepare an Incident Response Plan

Have a breach response plan ready that considers AI-related scenarios:

• What would you do if you discover an employee leaked data through AI?
• How would you act if an AI provider informs you they were attacked?
• How do you assess impact and notify customers or authorities?

Under GDPR: You generally must notify authorities within 72 hours if the breach is serious.

How to Evaluate an AI Provider’s Security

When evaluating AI providers, consider these key aspects:

Legal Compliance and Privacy Policies

• Do they comply with GDPR, CCPA, or other regulations?
• Do they offer a Data Processing Agreement (DPA)?
• Do they commit to notifying you in case of breach?

Platform Technical Security

Useful questions:

• Is data transmitted and stored encrypted?
• How do they control internal access?
• Have they passed penetration tests or audits?
• Where physically is data stored?

Data Usage and Model Training

Critical point: What does the provider do with the data you provide?

• Do they only process it temporarily or store it?
• Do they use it to train their AI model?
• Do they offer the option to exclude your data from training?

Track Record and Reputation

• Have they suffered security breaches in the past?
• Do they have reputable clients that vouch for their solution?
• Do they have industry certifications?

Support and Human Intervention

• Do they have 24/7 support?
• Do they offer Service Level Agreements (SLAs)?
• How do they escalate critical issues?

Security Checklist Before Implementing AI

Before deploying an AI solution, review this checklist:

• Define the use case and data involved: Classify data by sensitivity
• Conduct a DPIA if applicable: Document risks and mitigation measures
• Consult with legal department: Review privacy policies and contracts
• Evaluate the AI provider: Ask about security measures and certifications
• Implement access controls: Configure permissions and robust authentication
• Train users: Provide training before they use the tool
• Test with fictitious data: Validate in a controlled environment first
• Establish a monitoring plan: Define indicators and alerts
• Have an incident response plan ready: Everyone should know what to do if something happens

Real Cases: Security Breaches and Lessons Learned

Case 1: Samsung and the ChatGPT Leak (2023)

What happened: Samsung engineers used ChatGPT to help with programming tasks. In less than a month, they entered proprietary source code, confidential test patterns, and internal meeting transcripts. The data was stored on OpenAI’s external servers.

Result: Samsung temporarily banned the use of ChatGPT and similar tools company-wide.

Lessons:

• Establish clear policies on what’s allowed with corporate data
• Offer secure alternatives (enterprise versions or private instances)
• Educate your team by sharing real cases

Case 2: Voice Deepfake Scam (2019)

What happened: The CFO of an energy company in the UK received a call he believed came from the CEO of the German parent company. The voice sounded exactly like his boss. He was urged to transfer €220,000 to a Hungarian supplier. It turned out to be a voice deepfake generated with AI.

Lessons:

• Implement verification protocols for unusual requests
• Double approval for large transfers
• Train finance and management employees about these risks

Case 3: OpenAI/Mixpanel Breach (2023)

What happened: OpenAI suffered a breach through its analytics provider, Mixpanel. Account names, email addresses, and metadata of API users were exposed.

Result: OpenAI cut ties with Mixpanel immediately and notified affected customers.

Lessons:

• Also evaluate the security of your providers’ sub-providers
• Compartmentalize data: don’t give more information than necessary
• Have a plan in case a provider is abruptly discontinued

Case 4: Biased HR System

What happened: A company automated resume screening with AI trained on historical data. The model inherited biases and barely selected women for technical positions. Candidates filed complaints for discrimination.

Lessons:

• Review results periodically looking for disparities
• Implement human oversight in sensitive decisions
• Be transparent with people affected by AI

Conclusion: Protecting Today’s Trust for Tomorrow

The incorporation of artificial intelligence presents SMEs with an extraordinary opportunity to grow and compete. But as we’ve explored in this guide, there is no digital transformation without security.

Every piece of your customers’ data is a vote of trust they give you, and when using AI you must ensure you don’t betray them. Data security and privacy in AI is not a brake on innovation, it’s the pillar that supports it.

Key Points to Remember:

• 13% of organizations have already suffered breaches in AI systems
• The average cost of a breach is $4.44 million
• 97% of affected companies had no access controls for AI
• Complying with GDPR and data protection laws is mandatory, not optional
• The 7 best practices can be implemented starting today

We encourage you to put this knowledge into practice. Review the risks we’ve listed, apply the checklist to your next AI project, establish internal policies, train your staff.

Cybersecurity is a continuous process, not a final destination. Stay updated, because threats evolve just as solutions to combat them do.

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Need help securing your AI projects?

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