The cyber attack on the Office of Personnel Management (OPM) in the summer of 2015 was a watershed moment for federal IT leaders. The attack compromised the personally identifiable information (PII) and sensitive security information of nearly 22 million current and former federal employees and their families. While there’s been no evidence that the information has been used for criminal activity – so far – it has had long term ramifications for public trust in the ability of federal agencies according to Charles Phalen, director of the National Background Investigations Bureau.
However, based on a recent webinar hosted by Cylance, a leading next-generation cybersecurity firm, we should have a lot more confidence in the OPM based on its response to the attack. Since the breach, the OPM has moved swiftly since the breach to implement state of the art cyber solutions – including Artificial Intelligence (AI) and Machine Learning (ML) – to rapidly secure endpoints and enable the quick identification and containment of future attacks.
Lisa Schlosser Former Deputy Federal Chief Information Officer, Office of Management and Budget, Executive Office of the President and Robert Knake, Former White House Director of Cybersecurity Policy, and Senior Fellow at the Council on Foreign Relations, share their insights on the importance of using AI and ML in securing endpoints and how it can effectively defend federal agencies from sophisticated nation-state attacks.
As Cylance and cybersecurity visionaries within the federal government rewrite the rules of cyber protection and defense, it’s time for the rest of us to catch up on the impact that AI and ML will have on our ability to predict and prevent attacks on the nation’s most valuable asset – its data and protect it from compromise, destruction, and theft.
Interested in learning more? You can access the webinar featuring Lisa Schlosser and Robert Knake here. No time to watch? You can download a thought-provoking article on how artificial intelligence will secure the future here.
