Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Many organizations struggle to address network security vulnerabilities in time. By the time vulnerabilities are discovered, attackers may already be exploiting them across your infrastructure, especially in areas where visibility is limited. That delay leaves you scrambling patches get applied too late, remediation workflows are disjointed, and attackers can move laterally or exfiltrate data before containment begins.

AI marketing platforms have exploded in popularity, becoming everyday tools for creative teams in enterprises worldwide. Platforms like Simplified AI offer marketers the ability to generate content, clips, and campaigns at scale. For CISOs and IT leaders, approving such services often seems straightforward: allow access, whitelist the domain, and enable the marketing team to innovate.

Many security teams struggle to see the full scope of threats because network, endpoint, and cloud data remain siloed. Without unified visibility, detecting hidden attacks or spotting lateral movement is tough. Gaps between tools lead to fragmented signals, low-fidelity alerts, and slower investigations. That fragmented view can let attackers linger longer—and SOC analysts bounce between multiple interfaces just to piece together a coherent incident narrative.

We want to share an important update in light of the recent security incident involving Salesloft Drift, a third-party application connected to Salesforce. The issue centers on the misuse of OAuth tokens associated with the Drift app. Salesforce and other vendors identified unauthorized access between August 8 and 18, 2025. The incident has impacted hundreds of Salesforce customers. The Cato SASE Cloud Platform, services and infrastructure, were not affected in any way.

Every major technology wave reshapes enterprise security. The rise of the Internet gave us firewalls. The move to SaaS brought CASB and DLP. The migration to the cloud and rise of the hybrid workforce demanded a new architecture like SASE to enable network transformation. Today, the AI revolution is creating an entirely new attack surface – one that is as transformative as it is urgent.

In today’s digital landscape, web application security is more critical than ever. Most organizations rely on Cloud-Based Security Providers offering integrated Web Application Firewalls (WAFs) and Content Delivery Networks (CDNs), for shielding their assets from direct exposure and attacks such as SQL injection, XSS, and DDoS.

In cybersecurity, the biggest lie we tell ourselves is that our systems are safe because we think they’re not reachable. Firewalls, policies, and cloud rules look good on paper, but attackers don’t read your policies and they don’t trust your intentions. They test. If you aren’t testing from the outside too, you’re not defending, you’re guessing. And in this game, guessing gets you breached.

Imagine a retail chain, CaaT Networkstore, that wants to run a marketing campaign targeting its in-store customers. To do that, they need to know what types of devices their customers are using. They could survey the users, but a better, more accurate approach is to look at their free Wi-Fi logs and count the types of devices customers are using to connect to the network. If the store is small, the solution is fairly trivial.

Network teams often risk costly disruptions when aging or unsupported hardware slips under the radar. With Forward Networks’ Network Query Engine (NQE), you can proactively identify devices approaching End‑of‑Sale (EOS) or End‑of‑Life (EOL), plus enforce hardware/software compliance at scale. Get ahead of risks, reduce technical debt, and align your infrastructure with business goals—automatically.

Enterprise networks face a fundamental challenge: traditional signature-based detection methods fail against sophisticated threats that deliberately mimic legitimate traffic patterns. With networks generating terabytes of data daily and attack surfaces expanding through digital transformation, organizations need detection mechanisms that can identify subtle behavioral deviations without relying on known attack signatures.