Part 2: Social engineering, malware, and the future of cybersecurity in AI

Cybersecurity Threats & Actors - Nation-state actors are primarily motivated by geopolitical aims and espionage, often engaging in offensive cyberattacks to support warfare or prepositioning for potential conflicts [5][6] - Subnation-state actors and some nation-state activities are financially motivated, commonly using ransomware attacks to steal and encrypt data, demanding cryptocurrency for its release [9][10] - A gray market exists for zero-day vulnerabilities, with buyers including companies equipping law enforcement and governments, with some vulnerabilities worth millions of dollars [12][14] - AI is exacerbating social engineering risks by enabling deep fakes, making phishing attacks more tailored and effective, such as cloning voices for ransom demands or impersonating executives for financial fraud [30][32][33] Vulnerability Disclosure & Mitigation - Project Zero introduced a 90-day disclosure timeline for vulnerabilities, compelling companies to prioritize security patches to prevent exploitation by malicious actors [19][20] - Governments have been known to deliberately withhold vulnerability information for exploitation purposes, as exemplified by the Eternal Blue case [24] - Healthcare and critical infrastructure sectors often struggle with patch management due to the risk of disrupting essential services, leading to long-term vulnerabilities [29] - Multi-factor authentication and pass keys are emerging as strong defenses against phishing and password-related attacks, enhancing security and user experience [37][39][40] AI & Agent Security - Risk-based authentication, enhanced by AI, assesses user behavior to determine trust levels and adjust security friction accordingly, such as requiring multi-factor authentication based on anomalous activity [43][46] - The rise of AI agents acting on behalf of humans introduces new security challenges, requiring careful consideration of agent identity, permissions, and potential for misuse [50][51] - Contextual integrity is crucial for training AI agents to respect privacy norms and avoid disclosing sensitive data inappropriately, necessitating mechanisms for agents to seek permission before sharing information [57][58][59]