The Move Toward Post-Quantum Cryptography for Data Security
As security professionals work to future proof their organizations, they will need to have conversations about post-quantum cryptography.
“Post-quantum cryptography is coming,” Powell says. “I will be asking all of the vendors, ‘What is your roadmap for replacing your cryptography with quantum-safe cryptography?’”
Quantum computers, which are becoming more and more prevalent, are powerful enough to break encryption, either by brute force or other mechanisms that detect weaknesses, Salzberg says. “Because of that, we need to look at methodologies for stronger encryption.”
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“A lot of people think is a 10-years-from-now problem. It’s actually a today problem,” Powell adds.
Business Continuity in the Face of AI-Powered Cyberthreats
When it comes to AI, IT leaders at Black Hat USA will want to focus on advanced technologies that allow them to protect themselves from it.
“How do we protect ourselves from AI? We’re certainly seeing attackers use it quite a bit more in social engineering and deep fakes,” Salzberg says.
Trainings on risk quantification and identity analysis and solutions such as agentic security operations center analysts can prepare organizations to better protect their infrastructure.
Also on the agenda at Black Hat USA this year is a main stage presentation on the security benefits of the National Security Agency’s Continuous Autonomous Penetration Testing service. CAPT allows the Defense Industrial Base to find, fix and verify exploitable weaknesses, improving the entire organization’s cyber resilience.
DIVE DEEPER: Leverage adversary intelligence to thwart cyberattacks, experts say.
Other learnings from vendors such as Fortinet, Microsoft and Trend Micro can help IT professionals further their event planning around protecting themselves from AI.
However, that’s not the only AI security organizations need to plan for. They also need ways to secure the use of AI internally.
Measures to Secure Your Organization’s AI Tools and Workflows
Securing AI tools internally starts with strong data governance. “Data governance is so vital, and even more vital now for any AI initiative,” Salzberg says. “It’s required to feed AI good, clean information and to make sure it doesn’t have access to information it shouldn’t have.”