  From denial-of-service attacks to spreading of ransomware or other malware
across an organization's network, it is possible that manually operated
defenses are not able to respond in real time at the scale required, and when a
breach is detected and remediated the damage is already made. Autonomous cyber
defenses therefore become essential to mitigate the risk of successful attacks
and their damage, especially when the response time, effort and accuracy
required in those defenses is impractical or impossible through defenses
operated exclusively by humans. Autonomous agents have the potential to use ML
with large amounts of data about known cyberattacks as input, in order to learn
patterns and predict characteristics of future attacks. Moreover, learning from
past and present attacks enable defenses to adapt to new threats that share
characteristics with previous attacks. On the other hand, autonomous cyber
defenses introduce risks of unintended harm. Actions arising from autonomous
defense agents may have harmful consequences of functional, safety, security,
ethical, or moral nature. Here we focus on machine learning training,
algorithmic feedback, and algorithmic constraints, with the aim of motivating a
discussion on achieving trust in autonomous cyber defenses.
