AI security gaps: what practitioners need to do as adoption accelerates

TL;DR: Enterprise AI use rose from 55% to 78% in a year, while AI-related security incidents hit a record 233 in 2024 and the average breach cost reached $4.88 million, according to Mindgard. The gap is now governance failure, not experimentation failure: security must move to discovery, red teaming, and lifecycle controls before AI becomes ambient infrastructure.

Editorial analysis by NHI Mgmt Group, based on content published by Mindgard: “AI Security Statistics & Benchmarks: The Numbers Behind the AI Security Crisis”.

By the numbers:

• Enterprise AI use rose from 55% of organisations in 2023 to 78% in 2024, according to the Stanford HAI AI Index Report 2025 cited by Mindgard.
• AI-related security incidents reached a record 233 in 2024, according to the Stanford HAI AI Index Report 2025 cited by Mindgard.
• The average cost of an AI-driven breach reached $4.88 million, according to Harvard Business Review research cited by Mindgard.

Key questions

Q: How should organisations govern AI systems that can make consequential decisions?

A: Organisations should govern consequential AI systems with the same discipline used for high-risk identities: defined ownership, least privilege, logging, approval boundaries, and human override.

Q: Why do AI-assisted attacks increase application risk so quickly?

A: AI-assisted tooling reduces the effort required to find exposed endpoints, vary payloads, and test credentials at scale.

Q: What are the signs that an AI security programme is failing against autonomous threats?

A: A programme is failing when assurance depends on quarterly assessments, when new tools or prompts appear without retesting, and when findings are only measured as patch backlog.

Practitioner guidance

• Map every AI system and agent to an owner Build an inventory of models, agents, copilots, and embedded AI features across business units, then assign accountable owners for data access, tool use, and lifecycle decisions.
• Test AI workflows with adversarial scenarios Run red-team scenarios for prompt injection, data leakage, fake account abuse, and unsafe tool invocation before production rollout and after major workflow changes.
• Constrain AI delegation and tool scope Limit what each model or agent can call, read, write, or trigger, and review those boundaries as part of change control when the workflow expands.

Bottom line: The central risk is not AI adoption itself, but the gap between rapid deployment and the controls needed to govern access, data use, and delegated actions.

Explore further

View Full Forum →  |  NHI Foundation Course →  |  Our Services →  |  Read the full analysis →