Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Why Protecto Uses Tokens Instead of Synthetic Data

On the surface, synthetic data looks like the safer option. It’s not real. It doesn’t point to an actual person. It can be reversed if needed. And it keeps systems running without exposing sensitive values. That logic makes sense. Until you look at how systems actually behave. Protecto supports both reversible synthetic data and tokenization. Referential integrity can be preserved either way. Mapping back is not the hard part. The difference is not whether you can recover the original value.

What is Vibe Coding? #vibecoding #aisecurity #coding

Mend.io, formerly known as Whitesource, has over a decade of experience helping global organizations build world-class AppSec programs that reduce risk and accelerate development -– using tools built into the technologies that software and security teams already love. Our automated technology protects organizations from supply chain and malicious package attacks, vulnerabilities in open source and custom code, and open-source license risks.

Why AI-Driven Business Idea Discovery Makes More Sense

Finding the right business idea is one of the hardest parts of starting a business. Most people don't struggle because they lack motivation. They struggle because they don't know what kind of business actually suits them. A quick online search gives thousands of ideas: e-commerce, SaaS, content creation, agencies, coaching, marketplaces, and more. But very few of these sources help you answer a more important question.

How Can Creative AI Tools Help You Design Personalized Security Awareness Posters?

The truth is, the majority of the security awareness poster are forgettable. You may have encountered them: stock image of a pad lock, some bold printed text with a warning about phishing emails, perhaps a stock image of a person staring at his/her laptop in a concerned manner. They also become part of the office walls like a beige paint, and no one really listens to them.
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Security's Next Turning Point Is the Workforce

Cybersecurity is entering a turning point. It has less to do with new tools than a new reality: the workforce has changed. For years, security programs assumed risk lived in systems, controls, and configurations. People were the variable managed through policies, training, and best-effort awareness. That model was already under strain. Now it is being outpaced.

Tensorway: Redefining AI Software for Mission-Critical Applications

AI software is no longer limited to experiments, internal tools, or innovation labs. Today, it operates at the core of mission-critical systems - influencing financial decisions, controlling industrial processes, supporting healthcare workflows, and enabling real-time risk assessment. In these environments, failure is not an option, and reliability matters more than novelty.

Why Vulnerability Management Falls Short - And How Exposure Management Fixes It

Vulnerability management identifies weaknesses. Exposure management helps prioritize them based on real-world risk and context. Ed and Garrett unpack why traditional vulnerability programs struggle to drive real risk reduction. The challenge isn’t discovery. It’s prioritization and follow-through. Too often, vulnerabilities are treated as isolated IT tasks—handed off, tracked by SLAs, and stripped of the context that explains why they matter in the first place.

The Asymmetric Threat: Why AI API Traffic is Hard to Predict

The Asymmetric Threat: Why AI API Traffic is Hard to Predict As AI becomes more integrated into business operations, the way data moves through APIs is changing. In this clip from the A10 Networks webinar, "APIs are the Language of AI: Protecting Them is Critical," experts Jamison Utter and Carlo Alpuerto break down the concept of data asymmetry in AI.

Why Protecto Privacy Vault Is Ideal for Masking Structured Data

Picture this. You’re a data engineer at a healthcare company with millions of patient records in Snowflake. HIPAA requires you to protect PII before sharing data with researchers or running analytics. So you tokenize the data. And your system catches fire. Your joins break. Your ETL pipelines fail. BI dashboards return wrong results. ML model training jobs crash. All because something fundamental changed about your data architecture.