Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

The web is entering a new phase. Artificial intelligence is beginning to act on behalf of people rather than simply assisting them. AI agents are now browsing, comparing, and buying, taking on the decisions that once sat firmly in human hands. This marks the start of the agentic marketplace, an emerging ecosystem where autonomous systems interact, negotiate, and transact across digital platforms.

Picture your online shopping site overwhelmed with fake orders, your customer accounts being drained one after another, or your essential APIs flooded by an endless wave of automated attacks. This is the reality businesses face today—thanks to a fully automated army of cyber criminals determined to cause harm. In this digital bot invasion, businesses of all kinds are under urgent pressure to establish defenses that effectively fight this digital threat.

Today, we are announcing a new approach to catching bots: using models to provide behavioral anomaly detection unique to each bot management customer and stop sophisticated bot attacks.

Account takeover (ATO) is one of the most consistent and costly threats facing consumer-facing businesses in 2025. And this year, the problem has been supercharged by the Mother of All Breaches (MOAB), a credential leak containing 16 billion username and password combinations. It rarely begins with a breach of your own systems. More often, it starts with someone else’s data leak. Credentials are reused, recompiled, and redeployed across platforms you may not even realise are vulnerable.

We’re proud to announce that Netacea has once again successfully completed our SOC 2 Type II audit, marking our fifth consecutive year achieving this important milestone in data security and trust. SOC 2 compliance isn’t a checkbox exercise. It’s a rigorous, independent validation of how seriously we take the responsibility of protecting customer data. For five years running, Netacea has demonstrated our commitment to operating securely, reliably, and transparently.

What is LLM Scraping? We’re entering a new phase of the Internet, one that is increasingly shaped by generative AI. These systems need data, and lots of it. To meet this hunger, they scrape the web, pulling in everything from news articles and academic journals to product listings, metadata, and user-generated content. This practice, known as large language model (LLM) scraping, has moved far beyond traditional bots indexing public sites.

Botnets have become a core part of the infrastructure in today’s cybercrime ecosystem — not just as enablers of disruption, but as purpose-built networks engineered for profit, stealth, and scalability. Built from large networks of compromised devices and rented out via criminal marketplaces, botnets are now essential as-a-service components of any cyberfraudster’s toolkit. While the concept of a botnet is not new, their construction, use cases, and value have certainly advanced.

Retail fraud is becoming increasingly normalized in the US and UK as ‘refund hacks’ are promoted to consumers by organized crime gangs looking to recruit both knowing and unwitting digital mules. This positioning of fraudulent activity as a ‘refund hack’ deliberately hides its illegal nature. Combined with growing awareness of fraud techniques – both online and offline – it’s driving consumer acceptance of casual fraud. This is bad news for retailers.