Update home.md

Author: unamay

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 The cyber landscape is an evolving arms race and defenses need to stay one step or more ahead of threat actors. There is a constant need for innovation and adaptation because GENAI radically changes the playing field, enabling the threat actors to increase their power. Social and technical attacks and  threat actors' backend productivity are becoming more sophisticated and frequent. Ironically, defenders need AI to solve the emerging challenge driven by GENAI but, current solutions are incremental and lack innovation. They don’t solve user-centric questions, e.g. how can a threat actor reach **my** crown jewels? Or, is **my** network vulnerable to the most recent campaign of an active threat actor? 
 
-ALFA is redefining cybersecurity by uniquely using AI to solve its rapidly emerging challenges.  We take an [Adversarial Intelligence and Behavior lens]()  and use clear imperatives to drive our projects. We are driven by the questions defenders fundamentally want  to answer. Why was this threat actor doing what has been observed? How does my analysis fit into this threat actor’s plans and goal? What will this threat actor do next? Like puzzle pieces, our projects populate an ecosystem of advances. We have long term investments in projects like [BRON](http://bron.alfa.csail.mit.edu/info.html)  which supports threat actor profiling, malware analysis intelligence, and technique-based hunting by unifying critical open cyber threat intelligence resources (CTI) such as ATTACK, CVE and DEFEND into a single, bidirectional graph database, RIVALS which supports modeling cyber scenarios' coevolutionary arms races, and our agent intelligence effort emblemized by our [*LLMs Killed the SCRIPT-KIDDIE* report](https://arxiv.org/abs/2310.06936).  We have the acumen to seize an emerging situation. Hence a new investment in AI-security culture, pursuing evaluating and comparing organizations' perceptions of AI risks and offering actionable insights into cultural improvements.
+ALFA is redefining cybersecurity by uniquely using AI to solve its rapidly emerging challenges.  We take an [Adversarial Intelligence and Behavior lens](/unamay)  and use clear imperatives to drive our projects. We are driven by the questions defenders fundamentally want  to answer. Why was this threat actor doing what has been observed? How does my analysis fit into this threat actor’s plans and goal? What will this threat actor do next? Like puzzle pieces, our projects populate an ecosystem of advances. We have long term investments in projects like [BRON](http://bron.alfa.csail.mit.edu/info.html)  which supports threat actor profiling, malware analysis intelligence, and technique-based hunting by unifying critical open cyber threat intelligence resources (CTI) such as ATTACK, CVE and DEFEND into a single, bidirectional graph database, RIVALS which supports modeling cyber scenarios' coevolutionary arms races, and our agent intelligence effort emblemized by our [*LLMs Killed the SCRIPT-KIDDIE* report](https://arxiv.org/abs/2310.06936).  We have the acumen to seize an emerging situation. Hence a new investment in AI-security culture, pursuing evaluating and comparing organizations' perceptions of AI risks and offering actionable insights into cultural improvements.
 
 To round out our broad interest in Adversarial Intelligence and Behavior, we have renewed interest in taxation avoidance and an new interest in financial fraud. Stay tuned for updates.  Click here for our youtube channel with talks on our projects' current states.