Rethinking cyber incident SLAs in multi-vendor AI environments

Welcome reader to a 🔒 pro subscriber-only deep dive 🔒.

Brought to you by:

👣 Smallstep – Solves the other half of Zero Trust by securing Wi‑Fi, VPNs, ZTNA, SaaS apps, cloud APIs, and more with hardware-bound credentials backed by ACME Device Attestation

🏄‍♀️ Upwind Security – Real-time cloud security that connects runtime to build-time to stop threats and boost DevSecOps productivity

🔧 Endor Labs – Application security for the software development revolution, from ancient C++ code to bazel monorepos, and everything in between

🧠 Ridge Security – The AI-powered offensive security validation platform

Forwarded this email? Join 70,000 weekly readers by signing up now.

#OpenToWork? Try our AI Resume Builder to boost your chances of getting hired!

—

Get lifetime access to our deep dives, weekly cyber intel podcast report, premium content, AI Resume Builder, and more — all for just $799. Corporate plans are now available too.

Executive Summary

The rapid proliferation of artificial intelligence across enterprise environments has fundamentally altered the cybersecurity landscape. As of 2025, organizations increasingly rely on multiple AI vendors to power critical business functions, from threat detection to automated incident response. This multi-vendor AI ecosystem introduces unprecedented complexity in managing cyber incidents, rendering traditional Service Level Agreements (SLAs) inadequate for addressing the unique challenges posed by interconnected AI systems.

Recent data reveals that 77% of organizations have already experienced AI-related security breaches, while 87% express deep concern about AI-specific risks in vendor relationships. The convergence of multiple AI providers, each with proprietary models and varying security postures, creates cascading vulnerabilities that traditional SLAs fail to address. When an AI-driven security incident occurs, determining accountability across vendors becomes a complex exercise that can delay critical response times.

This white paper examines the urgent need to redesign cyber incident SLAs for the AI era. Key findings include:

• Traditional SLAs focus on availability metrics while ignoring AI-specific risks such as model poisoning, adversarial attacks, and data leakage through AI systems

• Multi-vendor AI environments create accountability gaps where no single vendor takes responsibility for cross-system failures

• New regulations including the EU AI Act, NIS2 Directive, and DORA are forcing organizations to rethink vendor accountability and incident response obligations

• Leading organizations are implementing AI-specific SLA clauses covering explainability requirements, adversarial resilience testing, and cross-vendor coordination protocols

For Chief Information Security Officers (CISOs), the path forward requires fundamental changes to how cyber incident SLAs are structured, negotiated, and enforced. This includes embedding AI-specific security requirements, redefining what constitutes an "incident" in AI contexts, and establishing clear accountability frameworks for multi-vendor environments. Organizations that fail to adapt their SLA strategies risk regulatory non-compliance, prolonged incident response times, and significant financial exposure when AI-driven security incidents inevitably occur.

Subscribe to CybersecurityHQ Newsletter to unlock the rest.