Our Take
Red teaming is now table stakes for production AI, not a nice-to-have; the spike in incidents proves the gap between lab safety and real-world attack surface.
Why it matters
As organisations deploy agentic systems connected to APIs and data stores, adversarial testing before launch cuts incident response time and builds the audit trail regulators (NIST AI RMF, EU AI Act) now expect. The window to catch vulnerabilities before they become breaches is closing.
Do this week
Security lead: map your deployed AI stack (models, agents, APIs, RAG pipelines) and select a red teaming provider that tests all three layers before end of month so you can remediate findings before the next audit cycle.
AI Incidents Are Accelerating—Red Teaming Is the Response
AI incidents rose sharply from 233 in 2024 to 362 in 2026 (source-cited data trend), signalling that adversarial testing before deployment is no longer optional. Red teaming recreates attack scenarios—prompt injection, data poisoning, jailbreak attempts, unauthorised API access—to expose vulnerabilities before production launch.
The practice mirrors offensive security in traditional infrastructure: teams use systematic, replicable attack techniques to map failure modes, then strengthen guardrails. Unlike theoretical safety assessments, red teaming observes how live models and agents actually respond to malicious inputs.
Compliance and Incident Response Both Depend on Pre-Launch Testing
Red teaming serves two constituencies at once. First, it supports regulatory alignment. Organisations can map red teaming findings to NIST AI RMF, ISO 42001, and EU AI Act requirements, creating the documented evidence that regulators now demand. Second, it shrinks mean-time-to-respond in production. When teams have already simulated failure modes, detection rules and containment procedures are pre-tuned.
The larger shift: as agentic systems connect to real APIs, data stores, and workflows, the attack surface expands beyond model output. Unauthorised data access, model drift, and bias failures now sit inside integrated systems. Testing that stops at the model boundary leaves most of the risk unexposed.
Three Established Providers
CBIZ Pivot Point Security combines manual red teaming with governance services, covering APIs, data stores, and network infrastructure alongside RAG and agentic workflows. Reply integrates threat modelling with continuous monitoring and supports EU AI Act compliance. Mindgard operates as an autonomous red team, replicating attacker techniques and embedding runtime defences.
No single leader dominates; differentiation lies in depth of attack simulation, breadth of stack coverage, and integration with internal security workflows. Vendor-published capabilities are not yet independently benchmarked.
How to Select and Deploy Red Teaming
Focus on stack breadth first. A provider that tests models alone but skips agents and APIs leaves your highest-risk surface untouched. Second, confirm that attack simulations reflect current adversarial techniques, not generic threat models. Third, verify alignment with your regulatory baseline (NIST AI RMF for US-first teams, EU AI Act for European deployments).
Ongoing monitoring matters more than one-time assessment. Regressions appear when models are fine-tuned or agents gain new tool access. Continuous testing catches these shifts before they reach production.
The selection criteria are straightforward: full-stack coverage, realistic attack depth, regulatory mapping, and continuous iteration. If a provider offers less than this, you are paying for partial visibility.