How to build enterprise resilience in the face of growing AI risk

New research from StackGen analyzing nearly 178,000 public technology incidents found AI-related incidents now account for more than one in 10 reported outages, roughly six times the rate in 2023.

AI agents have deleted data, databases , or live systems autonomously.

Those agents acted with valid credentials, meaning traditional monitoring did not identify anything unusual until the damage was done.

With AI becoming embedded in business processes across claims processing, coding, customer support, decision support, fraud detection, HR , risk analysis, and supply chain planning, outages and unintended outcomes are a growing risk throughout the enterprise.

And while the risk may feel unprecedented or novel, established resilience practice provides the path forward.

AI is creating often-unseen dependencies and risk AI-embedded business processes are just one aspect of enterprise AI risk.

AI is also accelerating cyber risk for enterprises.

Attackers can now employ AI to scale deepfakes, phishing, social engineering, reconnaissance, and develop exploits.

Additionally, AI systems may change behavior over time, creating drift and explainability gaps.

This is most likely when AI data sources, integrations, models, and prompts change.

Enterprise employees and teams may be using unapproved AI tools with sensitive information or in business-critical workflows.

This shadow AI also creates significant enterprise risk.

Meanwhile, AI adoption is creating operating dependencies faster than governance is maturing.

Recovery complexity adds another layer of risk.