#AI is reshaping every part of the enterprise, and leaders are being forced to manage more change than ever. From a security standpoint, it all starts with governance.
Strong governance. Clear policy. Targeted controls. That’s how enterprises adopt AI safely—and at scale.
As you deploy agentic AI and automation, pen testing becomes a strategic safeguard—ensuring your AI agents don’t unintentionally create new attack paths, expose sensitive data, or make autonomous decisions that put the enterprise at risk.
In the U.S., breaches are even more expensive, with average total costs around 5.09 million USD per incident in 2024. Organizations that invest in strong security testing and incident response capabilities save about 248,000 USD per breach on average
45% of all data breaches occur in the cloud, and public‑cloud breaches average about 5.17 million USD in costs. Through 2025, Gartner estimates that 99% of cloud security failures are due to misconfigurations, such as open storage buckets and excessive IAM permissions.
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#ABA Model Rules (e.g., Rules 1.1, 1.6, 5.3, 1.15) interpret competence and confidentiality to include understanding technology risks and taking #reasonable cybersecurity measures.
AI has made cyber both faster and more complex; managed security services are how most organizations plug into that AI‑powered security capability without having to build a full AI‑SOC and governance program from scratch.
We are entering a new era of security. As companies adopt agentic AI and platforms like Claude Mythos, it is evident that building AI agents creates a new attack surface.
The hype around agentic AI misses the importance of foundational security and AI governance. Most enterprises cannot implement AI without considering and managing cybersecurity risks.