Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Security operations centers (SOCs) have been drowning in a sea of alerts, raw logs, and siloed data for quite sometime now. A study by Morning Consult and IBM showed that 63% of alerts handled by SOCs daily are false positives or low priority, and analysts spend one-third of their day investigating these alerts. Yet, the information needed to detect, investigate, and respond to threats is often already present, just fragmented and buried. This is where AI steps in to rewrite the rules of engagement.

Businesses across all sectors, from finance to healthcare, hold valuable company data and intellectual property relating to their operations, employees, and customers. But this data has become a prime target for bad actors, including cybercriminals and malicious insiders who are constantly finding new ways to steal it for profit or harm.

AI systems depend heavily on secure web applications, APIs, and third-party data sources, but these interfaces are often the most exposed and exploited. The NIST AI Risk Management Framework (AI RMF 1.0) helps organizations govern, map, measure, and manage AI-related risks comprehensively.

Why blocking should always be the final step, not the first instinct Artificial intelligence has changed the way people discover information online. Instead of scrolling through ten blue links, millions now ask chat assistants for instant answers. Those assistants rely on automated software known as AI crawlers. These crawlers visit public websites, collect text, code, and metadata, and then feed that material into large language models.