AI is creating new cybersecurity risks — and naturally, an entire industry is emerging to sell us new tools to manage them.

AI security posture management. AI gateways. AI firewalls. Agent security. Runtime protection. Model security. AI governance platforms.

Some of these technologies will be essential. But before adding another platform, another dashboard and another acronym to the security stack, there's a question worth asking:

How much of your AI security problem can you solve with the tools you already own?

In this episode of AI Security Multipliers, Steve looks beyond the AI security hype to examine how many enterprise AI risks are actually familiar cybersecurity problems wearing a very clever new interface.

Excessive AI access is still an identity problem. Sensitive data being shared with AI is still a data protection problem. An overprivileged AI agent is still a privileged access problem. An insecure AI workload is still a cloud security problem. And AI generated malicious code executing on a laptop is still something endpoint security should care about.

We explore how existing capabilities including IAM, MFA, privileged access management, DLP, endpoint security, cloud security, SIEM, Zero Trust and security monitoring can form much of the foundation for securing enterprise AI.

There are genuinely new AI security risks that require new capabilities. But the answer shouldn't be to build an entirely separate cybersecurity stack for AI.

Start with the risk. Map the controls you already have. Identify the real gaps. Then buy technology to fill those gaps.

Because there's a difference between security architecture and shopping.

Topics include: AI security, enterprise AI, generative AI security, agentic AI, AI agents, cybersecurity, AI governance, identity security, IAM, DLP, Zero Trust, SIEM, cloud security, endpoint security, AI security tools, security architecture and cybersecurity tool sprawl.

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