The 90-Day AI Security Reality

AI adoption is accelerating, but enterprise security controls aren't always keeping pace. As organizations introduce AI agents, internal MCP servers, and new AI-powered workflows, they face growing challenges around identity, access, sensitive data, and API governance.

The challenge isn't necessarily building an entirely new AI security stack. It's ensuring that existing security controls can support an increasingly AI-driven operating environment.

In The 90-Day AI Security Reality, Dr. Chase Cunningham examines why security leaders facing aggressive 90-day AI mandates should strengthen foundational security practices to support AI adoption without introducing unnecessary risk.

Core Capabilities

Full-Stack Observability:

End-to-end visibility from mainframe to microservices to mobile with all telemetry (metrics, logs, traces, events) in context.

Davis® AI:

Advanced root cause analysis, anomaly detection, and remediation guidance at scale.

Out-of-the-Box Alert Coverage & Davis® AI:

Detects issues others miss by combining broad default alert coverage with causal AI to uncover problems before users notice.

Grail™ + DQL:

High-speed, schema-on-read querying of logs, events, business, and security data.

Application Security:

Real-time vulnerability detection, exploit prevention, and Kubernetes Security Posture Management (KSPM).

Custom Workflows:

Build tailored automation and platform extensions for unique business requirements.

Open Standards & Integrations:

Built-in support for OpenTelemetry, OpenSLO, and 750+ supported technologies.

Auto-Discovery & Auto-Instrumentation:

OneAgent® detects, instruments, and baselines every component automatically.

Auto-Baselining & Continuous Updates:

Learns “normal” behavior, adapts dynamically, and updates agents automatically to minimize maintenance.

Real-User & Synthetic Monitoring:

Advanced digital experience monitoring across web, mobile, and API layers.

Cloud-Native & Kubernetes Insights:

Real-time observability for microservices, serverless workloads, and container platforms.

Key Questions About Enterprise AI Security

Why is AI adoption straining enterprise security?

As organizations deploy AI agents, MCP servers, and AI-powered workflows, they introduce new identities, access requirements, and data flows that existing security programs may not be fully equipped to govern.

Why are traditional security controls still relevant to AI?

Many AI security challenges stem from familiar gaps in identity management, access controls, data loss prevention (DLP), and API governance. The report examines why these foundational security disciplines remain critical in emerging AI environments.

How can organizations secure AI adoption without slowing innovation?

Organizations can accelerate AI deployment by building established security controls into development workflows from the start. The report explores why consistent identity, access, DLP, and API governance practices can help teams move toward production while keeping policy enforcement and auditability in place.

Secure AI Adoption with Arctiq

As organizations expand their use of AI, understanding how existing security controls apply to emerging technologies is increasingly important. Connect with Arctiq to discuss your AI security priorities and challenges.

Talk to an Arctiq Security Expert

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The 90-Day
AI Security Reality

Why “Going All-In on AI” Still Comes Back to Identity, Access, DLP, and API Governance

AI Adoption Is Outpacing Security Readiness

Research highlighted in the report reveals:

78%

7%

97%