Cyera Buys Ryft: How PMEs Can Secure AI Data in 2024

Why Cyera’s €120M Ryft Buy Matters to Your PME

Last week, Cyera—a cybersecurity leader—dropped a bombshell: it’s acquiring Ryft, an Israeli startup specializing in automated, secure data management for AI systems. The deal, valued between €92M and €120M, is massive for a company that raised just €6.8M. Why? Because it signals a critical shift: AI is no longer optional for PMEs, but securing its data is now a survival issue.

Here’s the hard truth: If your PME handles customer, financial, or operational data, your AI tools are already processing it. Without proper safeguards, you’re exposing yourself to breaches, compliance fines, and reputational damage. The Ryft acquisition proves that even tech giants are racing to plug the gaps—so how can your business keep up without spending millions?

Spoiler: You don’t need a €120M budget. You need a strategy that scales with your growth. Let’s break it down.

AI Data Security: The Hidden Cost of “Good Enough”

Ryft’s technology automates data classification, access controls, and compliance checks—critical for AI systems that ingest everything from emails to transaction logs. But here’s the kicker: 60% of PMEs admit their AI tools lack proper data governance (Gartner, 2024). The result? A 2023 IBM report found that the average cost of a data breach for SMEs hit €3.3M—up 15% from the previous year.

Take a French e-commerce PME, for example. After integrating an AI chatbot to handle customer inquiries, it discovered—too late—that the bot was storing unencrypted payment data in a third-party cloud. The fine? €500K for violating GDPR. Ryft’s tech would have flagged the risk upfront by automatically classifying sensitive data and restricting its use in AI models.

Your move: Audit your AI tools today. Ask: Where is our data stored? Who can access it? Is it encrypted in transit and at rest? If the answers are vague, you’re already playing with fire.

Automation > Manual Checks: The Ryft Advantage for PMEs

Cyera’s bet on Ryft isn’t about flashy tech—it’s about automation. Ryft’s platform uses AI to continuously monitor data flows, detect anomalies, and enforce policies in real time. For PMEs, this means:

• 10x faster compliance: Ryft’s technology cuts GDPR audit prep time from weeks to hours by auto-generating reports. A German logistics PME reduced its compliance workload by 85% after adopting similar tools.
• Zero-trust for AI: Ryft enables “just-in-time” access, ensuring AI models only use data they’re explicitly authorized to process. This slashes the risk of shadow AI—where employees feed sensitive data into unvetted tools.
• Cost efficiency: Unlike Cyera’s enterprise solutions, Ryft’s tech is designed for mid-market budgets. PMEs can deploy it as a SaaS model, paying ~€5K/month instead of €50K+ for custom builds.

Real-world impact: A Swiss fintech PME used Ryft’s AI to flag that 12% of its customer data was being misclassified as “low-risk.” By reclassifying it, they avoided a €1.2M fine under Switzerland’s revised data protection laws.

Pro tip: Start small. Pick one AI use case (e.g., customer support chatbots) and apply automated data governance to it. Measure the ROI before scaling.

How to Future-Proof Your PME’s AI Data Security (Without a €120M Budget)

The Ryft-Cyera deal is a wake-up call for PMEs: AI adoption is accelerating, but so are the risks. Here’s how to act now:

1. Start with a Data Inventory

Before you secure anything, know what you’re securing. Use free tools like Veritas Data Insight or Microsoft Purview to map where your data lives, who accesses it, and how it’s used in AI. A French manufacturing PME discovered that 30% of its AI models relied on outdated supplier contracts—exposing it to legal risks.

2. Enforce the Principle of Least Privilege

Ryft’s tech automates this, but you can replicate it manually:

• Restrict AI tools to only the data they need (e.g., don’t let your marketing chatbot access HR files).
• Use role-based access controls (RBAC) to limit who can feed data into AI models.
• Rotate credentials every 90 days (yes, even for AI connectors).

A UK retail chain avoided a breach when its AI inventory system flagged an employee trying to export customer emails to a personal drive—thanks to automated privilege checks.

3. Layer on Encryption and Tokenization

Even if data is stolen, encryption ensures it’s useless. Tools like Virtru or AWS KMS encrypt data before it hits AI models. For extra security, tokenize sensitive fields (e.g., replace credit card numbers with random tokens). A Dutch SaaS PME cut its breach risk by 90% after implementing tokenization for customer data in its AI chatbot.

4. Test Your Defenses (Before Hackers Do)

Run penetration tests on your AI tools using frameworks like OWASP Top 10. Focus on:

• Data poisoning attacks (where hackers corrupt training data).
• Prompt injection (malicious inputs tricking AI into leaking data).
• Shadow AI risks (unapproved tools processing sensitive data).

A Belgian logistics PME found its AI route-optimization tool was vulnerable to prompt injection—hackers could reroute deliveries by feeding it fake traffic data. Fixing it cost €15K; the alternative would’ve been €2.1M in lost goods.

Your Next Step: Don’t Wait for a Breach to Act

The Ryft-Cyera deal proves one thing: AI data security is no longer a “nice-to-have”—it’s a prerequisite for growth. But here’s the good news: You don’t need a €120M war chest to protect your PME. You need a pragmatic, scalable approach.

Start by asking yourself:

• What’s the most sensitive data my AI tools touch?
• Where are the gaps in my current security?
• How can I automate governance without hiring a team of experts?

If these questions keep you up at night, it’s time for a reality check. Our free AI security diagnostic can help you identify risks in under 30 minutes—no sales pitch, just actionable insights. Because in the AI era, securing your data isn’t just about avoiding fines—it’s about staying in the game.

Ready to future-proof your PME? Book your diagnostic now.