Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Josh Rector is the Compliance Director, Public Sector at Ace of Cloud, a security and compliance consulting firm, certified CMMC Third-Party Assessor Organization (C3PAO), and Registered Provider Organization (RPO). With more than a decade of experience in cybersecurity compliance, he has worked both sides of the assessment table, leading internal and external assessments, serving as ISSO for systems at federal agencies, and guiding cloud service providers through the FedRAMP authorization process.

In 2025, organizations spent billions on security, deploying EDR/XDR, SASE, firewalls, identity platforms, email security, web security, and more. And yet, breaches persist. The reason often is not a zero-day, an advanced persistent threat, or a cutting-edge exploit. It is far more mundane. Misconfigurations across identity, endpoint, network, and email/web security controls remain among the top root causes of incidents.

Note: The core architecture for this pattern was introduced by Isaac Hawley from Tigera. If you are building an AI agent that relies on tool calling, complex routing, or the Model Context Protocol (MCP), you’re not just building a chatbot anymore. You are building an autonomous system with access to your internal APIs. With that power comes a massive security and governance headache, and AI agent security testing is where most teams hit a wall.

Organisations deploying agents face a challenge: the predominant AI frameworks most organisations rely on do not explicitly address agentic AI. This is true for the big three: ISO 42001, NIST's AI Risk Management Framework (RMF), and the EU AI Act.

AI application security protects AI-powered apps, including those powered by large language models ( LLMs), from unique threats like prompt injection, data poisoning, and model theft. It achieves this by securing the entire lifecycle, including code, data, algorithms, and APIs, using specialized tools and processes that go beyond traditional security measures. It involves securing the AI model’s behavior, training data, and outputs.

The cybersecurity landscape is facing its own perfect storm: AI-powered attacks coupled with resource constraints and regulator pressure, demanding a fundamental shift in SecOps to rise above. With AI showing no signs of slowing down, these issues are not fleeting. They are here to stay, and it is our responsibility to meet them head-on with efficient, AI-powered solutions that allow SecOps teams to conquer the world’s most innovative attacks.