CXO Think Tank

Technology executives are laser-focused on operating and securing the critical aspects of their business. Continuity, compliance, and risk management cannot come at the expense of high performance. When faced with the same responsibility for their family, security and privacy compromises unfortunately get made. They are simply stretched too thin.

Partners, children, and parents are reusing passwords that have already been part of a data breach. Security and privacy settings on Instagram, TikTok, Facebook, Snapchat, Venmo, LinkedIn, and WhatsApp are not met with the same rigor as AWS, Salesforce or DocuSign in the enterprise. MFA adoption is low.

Learn how a proactive consumer Cyber Health company is delivering cybersecurity, privacy, and digital hygiene to all.

If digital transformation is a journey, then data is the fuel that transports your company from here to there. Comprehending and creating insight from data is transformational. Being able to gather and analyze data to generate a competitive edge, creates new opportunities and promotes innovation. However, to support a pipeline of up-to-date and competitive modern data and analytics, a sound foundation and architecture must be constructed.

The combination of rogue insiders and external attackers makes SaaS application detection a massive pain point for enterprises, particularly within core business applications. External attackers leverage stolen credentials to impersonate an insider and connect to applications, while at the same time insiders are not sufficiently monitored. Such examples could include a fraudster’s takeover via social engineering, or incorrect implementation by an employee, or a doctor accessing celebrity patient medical data, or a salesperson downloading a report of all customers before switching to work for a competitor. Even after the enterprise receives a complaint or is otherwise suspicious, detection of these breaches usually consists of manual sifting through tons of log data from multiple sources. RevealSecurity’s CRO, Mark Fullbrook, will explore the growing challenge of SaaS application detection, explain why current detection solutions are usually ineffective, and share solutions using real customer examples.

According to Gartner 79% of companies have experienced at least one cloud data breach during the pandemic. Remote work is here to stay, and the concept of securing a perimeter has essentially ended. Traditional application security measures are broken. The need to innovate faster and shift to cloud-native application architectures isn’t just driving complexity, it’s creating significant vulnerability blind spots also.

Under the thumb of the pandemic, enterprises and IT leaders had to look for tech solutions that were resilient and agile to empower the remote workforce. To sustain business continuity plans, organizations shifted workloads to the cloud. As much as cloud adoption offers flexibility and productivity, it also exposes organizations to cyber threats and data breaches. So the question remains if moving to the cloud is the right thing to do and if so how to protect it from the new risks given that most organizations believe that application security should be completely automated to keep pace with dynamic clouds and rapid software development practices.

The industry is spending record amounts on cybersecurity tooling, but somehow CISOs still are at times left scrambling to respond to the vulnerabilities like Log4j. Assuming that these types of critical and far-reaching events are inevitable, how can CISOs further improve their organization’s preparedness for future cyberattacks?

This panel will discuss potential strategies for determining the critical security controls - both technology and behavioral - that can minimize cyber-risks and give the organization the competitive advantage to grow and innovate. We will explore frameworks for measuring the efficacy of cybersecurity investments, and KPIs that show the board the investment is safeguarding the company's digital infrastructure for the long term.

The terms "Artificial Intelligence" and "Advanced Machine Learning" are often thought of interchangeably. While there is a relationship between AI and AML, to say they are the same thing is an oversimplification and misclassification. Rather, one begets the other with AI being the basic principle upon which AML is developed.

As AI begins to mature and migrate away from purely advanced mathematical operations into decision making paradigms, AML steps forward as the predictive ability of machines to process vast quantities of data. As data and analytics becomes foundational to the way every business operates, AI and AML will become foundational capabilities.