Three separate research releases landed this week, each examining AI governance from a different angle.
Put them side by side and a consistent pattern emerges that should give most CX leaders pause.
Worldwide AI spending is projected to reach $2.52 trillion in 2026. The amount allocated to AI trust, risk, and security management is approximately $3.43 billion.
That works out to roughly one dollar in security for every $735 spent on capability, according to TELUS Digital's GenAI Safety Model Benchmark, built on more than 620,000 adversarial tests across 34 AI models from 10 global providers.
Bret Kinsella, General Manager and Senior Vice President of Fuel iX™ at TELUS Digital, frames the consequences of that imbalance plainly:
“The real risk isn't that AI models have vulnerabilities. It's that most organizations have no way of knowing which vulnerabilities apply to them.”
That's the thread running through all three studies. Organizations are deploying AI agents at scale and at speed, and the governance infrastructure behind those deployments is not keeping pace with the risk they're accumulating.
A Problem that Has Already Arrived
The TELUS Digital benchmark found that 86% of organizations have already experienced an AI-related security incident.
Across the 34 models tested, vulnerability rates ranged from 1.3% to 93%. Models with built-in reasoning capabilities were substantially harder to exploit, recording a 19.9% vulnerability rate compared to 55.1% for models without that capability.
This gap has direct implications for how enterprises approach model selection.
The risk categories carrying the most severity were privacy exploitation, fraud, and cybersecurity threats.
Researchers also flagged what they call the "refuse-but-engage" pattern, where models that initially decline a harmful request but then provide related information that could be put to harmful use anyway. That's not the kind of behavior a standard launch-day compliance check is designed to catch.
Sinch's AI Production Paradox research puts numbers to what those vulnerabilities look like once an agent is running in production.
Of organizations that have deployed AI communications agents, 74% have been forced to roll them back or shut them down entirely. PII or customer data exposure is the leading cause, cited by 31% of organizations. Hallucination or brand risk follows at 22%.
It’s important to understand that these are not failures that stay tucked away in internal logs; they play out on customer-facing channels, in live interactions, in real time.
When they do, the technical incident becomes a customer trust event, and those are considerably harder to recover from.
Confidence Doesn't Protect You
The Sinch data contains a finding that is easy to skim past but deserves attention.
90% of enterprise decision-makers describe themselves as confident in their AI agent readiness.
Among those same organizations, 75% have experienced at least one governance rollback. Confidence, it turns out, has essentially no correlation with governance outcomes.
More striking still, among organizations that describe their guardrails as fully mature, the rollback rate climbs to 81%, above the 74% average.
Sinch believes that this issue relates to the fact that more mature governance programs carry better instrumentation, and better instrumentation means they can detect failures that less mature organizations simply never see.
The companies reporting zero governance failures may not be running cleaner AI deployments. They may be operating with less visibility into what's actually happening.
TELUS Digital reaches a similar conclusion from a testing angle. The benchmark argues against point-in-time safety evaluations, making a direct case for continuous, automated testing built into developer workflows rather than periodic spot-checks.
In a healthcare deployment cited in the research, that approach cut testing time by 97% while achieving 99.6% accuracy in vulnerability identification.
As Kinsella puts it:
“Enterprises need to move from spot-checking GenAI solutions at launch to testing on an ongoing basis, or they're leaving vulnerabilities exposed that represent risk that could be avoided.”
From a practical perspective, the question for any leadership team isn't ‘Are we confident in our AI readiness?’ It's whether they would know about a failure before their customers did, and how fast.
