Security leaders top 10 takeaways for 2024

This year has shown that security teams must play an instrumental role in countering deepfake attacks by helping organizations better understand the risks and educating employees. “Using AI and machine learning can help supercharge efforts, helping teams make decisions and counter attacks by leveraging massive amounts of data,” she says.

Third-party threats have become more complex and diffuse

Growing third-party dependency continues to incentivize breaches that compromise user communities and at the same time, they’ve become more complex across different environments, according to Bethany De Lude, CISO at The Carlyle Group.

“As companies have adopted multi-cloud and SaaS-based business models, new challenges have emerged in managing risk across an information landscape defined by identity — and not a traditionally controlled edge,” she says.

In response, De Lude believes that new, pragmatic approaches to data and vendor management will emerge that take into account the changing boundaries and the way security increasingly centers on who has access to data and systems, rather than where those systems are located.

“They’ll need to address the way modern businesses operate across a complex, interconnected and distributed environment,” she says. 

AI and automation reshaped vulnerability management

This year showed how new tools that leverage AI for automated Q/A and regression testing at scale are reducing the burden on teams and accelerating safe, effective remediation processes, according to Rick Doten, VP, information security and CISO at Carolina Complete Health.

“These remediation workflow tools support prioritization, normalization, and de-duplicating of findings to route them to the appropriate team, and even create tickets to assign to specific people,” he says.

Although this can already be done with security orchestration, automation, and response (SOAR) tools, it requires people to create automation scripts and the process and workflow to support the automation.

AI-backed tools address resource limitations and the challenge of responsibility to fix the findings across many teams that might have different remediation workflows and ticketing systems. “With the dynamic nature of cloud environments, it’s [AI tools are] important because we have tens of thousands of findings to be remediated in workloads,” Doten says.