Targeted industrial cyberattacks are increasing at an alarming rate, especially within the manufacturing sector, costing companies an average of $2M per breach, this doesn’t even consider brand damage, downtime, penalties, or potential lawsuits. On average hackers are moving silently in networks 6 months before they are found. Sound scary? Well yeah, it is! Don’t worry we’re here to help.
The phishing threat has advanced. Stopping these threats requires a solution that’s built from the ground up using Artificial Intelligence (AI) and Machine Learning (ML). The architecture of yesterday’s rules and signature-based solutions simply are not equipped to handle these threats. In fact, of today’s advanced phishing threats, 51% require AI/ML to identify and stop. AI is clearly essential for cybersecurity. But the type of AI matters.
WannaCry, NotPetya, and TRITON demonstrate that ICS and IIoT networks continue to be soft targets for cyberattacks, increasing the risk of costly downtime, safety failures, environmental incidents, and theft of sensitive intellectual property. NIST and the NCCoE recently published a NIST Interagency Report (NISTIR) demonstrating how off-the-shelf, ICS-aware behavioral anomaly detection (BAD) effectively reduces cyber risk for manufacturing organizations, without impacting OT networks, as well as risk from equipment malfunctions.
Mobile operating systems (OSs) are fundamentally different from other endpoint OSs. Simply porting a security solution developed for a different platform over to mobile is inadequate and can leave your enterprise vulnerable. Effective enterprise mobile security requires a purpose-built solution that: Can protect mobile devices against known and unknown threats. Operates effectively even when an attacker controls the network.