Voltar 
Netskope continues to advance inline threat protection capabilities and has improved its detection and blocking of malware and phishing attacks while also lowering and improving its false positive rate in the latest AV-TEST Report. In every part of the testing, Netskope improved.
Today more than half of user egress traffic involves applications and cloud services rather than websites, and where more than half of threats are delivered knowing these popular domains are allowed, or worse yet bypassed from inline threat inspection altogether. Relying on endpoint protection for executable malware leaves a blind spot for fileless attacks using fake login forms and phishing techniques hosted in applications and cloud services that require inline content and context inspection to protect users. This dynamic changes the playing field for threat protection testing and what must be prioritized with respect to detection speed and efficacy scores.
First, user experience is number one today with high performance access to SaaS applications, cloud services, and websites from any location and device. This provides a single pass TLS inspection cloud service about 10ms to decode and detect unknown zero-day threats in real-time for egress traffic. The innovations of generative AI and machine learning add a new set of detection engines inline for real-time detection with the key requirement of providing the content to these new defenses. If you bypass inspection of popular office suites or rely on legacy defenses unable to decode and inspect inline applications and cloud services, the attackers are ahead of you. If the content is not inspected inline, new AI/ML-based defenses cannot help you for inline real-time threat or data protection.
Attacks are shorter lived today, changing behavior frequently to avoid detection and often targeting specific users. The decades old defense concept of herd immunity, of being part of a large herd where one member gets infected so the herd can learn about the infection (i.e., patient zero) and then provide signature updates to protect others, assuming you have time on your side. To keep the gate closed on known threats, the herd mentality still works. However, for new unknown and zero-day threats you need to focus on threat efficacy in real-time at time-zero (T+0) for the breadth of executable (PE files), non-executable (non-PE files), and phishing attacks.
To show how quickly a threat protection defense can learn about unknown threats from T+0, AV-TEST also analyzed T+1-hour detection rates. Once you expand to T+4 hours or longer the herd effect factors into the efficacy results with threat intel updates. Threat efficacy reports must clearly call out T+0, T+1, and T+4 results so you can understand real-time threat efficacy and protection, and how fast the protection learns about new attacks. Simply put, you want the best T+0 real-time detection rating with the lowest false positive rating, backed by the best T+1 detection rating to quickly learn and block new attacks.
Once you get to T+4 detection ratings or longer, vendor herd immunity comes into effect and efficacy should be roughly on par unless the vendor has a very poorly-designed solution and/or they are not investing in valuable threat intel feeds. Single detection results in threat efficacy test reports are often unlabeled T+4 results or longer, showing the highest percentages possible due to herd threat intel sharing and masking the T+0 reality for real-time detection of the solution. Ask for the chart below when analyzing and comparing threat efficacy rates.
Digging deeper into the details, it’s important to realize that an inline defense must quickly inspect content for unknown and zero-day threats and does not have the luxury of time like endpoint protection solutions that monitor the file system, directory, and process tree. The inline defense does have an advantage in its ability to decode and inspect application and cloud service content, which pairs well with AI/ML defenses that require content an endpoint cannot always provide. A similar advantage emerges versus legacy sandboxing focused on executables in a virtual OS that is unlikely to decode content for apps and cloud services. For example, AI/ML defenses can detect encrypted files, compromised access and devices, phishing pages, and fake forms, plus many other content and context related variables of a threat. Attacks work in kill chains where detecting earlier stages is preferred when possible. For example, ransomware kill chain stages may include compromised remote access or phishing to then enable malware delivery. Detecting phishing attacks in real-time can stop the ransomware kill chain before data encryption, exfiltration, and malware delivery.
To put the concepts above to the test, Net