We can see the threat developing almost in real-time. Adversaries are deploying new malware with just-in-time AI that dynamically generates malicious scripts and obfuscates code mid-execution to evade detection. They use sophisticated vishing and deepfakes for identity theft and business email compromise. We even see unauthorized AI tools lead to the rise of shadow agents.
Defending against AI powered security threats requires more than accelerating current security practices; it means stepping back and beginning with the security foundation and layered defenses. It’s critically important to build and use a layered defense with the right guardrails — foundational cybersecurity building blocks that we’ve been investing in for years.
Doubling down on this foundation: technologies like multi-factor authentication (MFA), Zero Trust frameworks, consistent system patching, and comprehensive detection and response. Collectively, these technologies reduce the attack surface and contribute to the deep context that defensive AI needs to be a business enabler — and create the necessary conditions for successful AI-powered defenses.
Revolutionizing vulnerability management
In just a few short years, identifying and fixing vulnerabilities has evolved from a mostly laborious, manual process to one driven by AI tools discovering vulnerabilities at volumes never seen before. Further, the time to exploit window has essentially been eliminated.
However, it’s not enough to merely discover vulnerabilities, especially at today’s volumes. You still need to prioritize fixing those that have the most critical impact on your systems and networks first, and that necessitates an equally-rapid response in smart mitigation.
Organizations use multiple models to scan for flaws and then suggest high-quality code fixes that engineers can quickly move into production, leveraging capabilities like AI Threat Defense. AI allows us to automate the entire software development lifecycle, from discovery to testing and deployment, ensuring that our defensive posture evolves faster than the threats targeting us.
Enhancing threat modeling
We’re also seeing the fundamental concept of threat modeling have an outsized impact. Doing threat modeling well requires bringing context together from your code, your cloud architecture, system design, and network pathways.
While it isn’t easy, using AI can scale our ability to bring that data together into a coherent picture. Teams have been experimenting with multi-AI models to collect system information and enumerate threats.
As I noted in June, engineering teams at Google Cloud now route product launches through an agent-based security review pipeline. High-risk indicators automatically get flagged for human review, while we’ve replaced static threat models with dynamic product dossiers that update in real-time.
The CISO as a strategic business leader
The most effective security leaders that I know today are more than just technologists: They are strategic business leaders. The intense global focus on AI vulnerabilities has brought cybersecurity to the forefront of boardroom and executive attention like never before.
This visibility is an opportunity to lead. We CISOs are expected to communicate with clarity, from the board to the C-suite to the security teams who look to them on a daily basis, demonstrating their ability as capable strategists who can navigate the complexities of AI while safeguarding the organization’s growth.
By aligning security fundamentals with business objectives and using AI to enhance defense, we can lead our organizations securely into the future.
To learn more about building and maintaining strong security foundations in the AI era, read our newest Defender’s Advantage: Cyber Snapshot Report.
