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Security is fundamentally about knowing what a system is supposed to do, then catching it when it does something else. For software with deterministic execution paths, that is a tractable problem. For AI agents, it is not. An agent does not follow a fixed code path. It reasons toward a goal, selects tools based on that reasoning, and adapts its next action based on what those tools return. The same input can produce a different sequence of actions depending on context, session history, and what an external tool happened to return. Behavior is the attack surface, and behavior changes. Traditional proxies and AI firewalls were built to inspect content: what a user sent, what a model returned. Intent security asks a different question: is this agent doing what it was built to do, in this context, for this user, right now? Answering that requires building a behavioral baseline for every agent and measuring deviation from it continuously. That is the problem Lasso was built to solve....

Cybersecurity is undergoing a necessary transformation from reacting to threats as they arise to proactively anticipating and addressing them through Threat-Informed Defense (TID) . This shift emphasizes operational discipline over accumulating more tools. It involves using threat intelligence to streamline existing technologies, enhance the quality of security signals, and focus efforts on the threats most relevant to each organization. The goal is to continuously identify and close security gaps by combining insights from external threat data with internal defense capabilities. How do you put TID into practice? The team at  Filigran has broken down the TID framework into a six-stage pipeline to develop actionable chunks for cybersecurity leaders. In this article, we share the details so that your security teams can leverage it too to support TID. What is Threat-Informed Defense? First advocated by  MITRE , Threat-Informed Defense (TID) leverages MITRE ATT&CK framewo...

The surge in DDoS attack traffic this year has been driven in part by the rapid expansion of IoT devices - from smart watches and home appliances to cars, hundreds of millions of new devices are joining the global internet. Many of these new devices feature poor security and are easily added to attacker's pool of botnets.  It is true that the DDoS threat grows alongside internet expansion. But the relationship isn't linear. The true catalyst behind this surge lies in the mass availability of botnet-for-hire platforms and low-barrier attack tools. Meanwhile, the number of high-value targets – such as financial institutions, governments, and critical infrastructure – remains relatively fixed. The result is a growing imbalance, in which more attackers are armed with more tools - targeting the same essential services with increasing frequency and complexity. How AI Makes DDoS More Dangerous  AI and machine learning are impacting the evolution of DDoS strategies and tactics. T...