AI Is Outpacing Compliance. Forrester’s 2026 Predictions Show What Comes Next

CISOs are now managing agentic systems that can make decisions and take action with limited human input. At the same time, regulations are changing, vendors are altering how they handle data, and infrastructure risks are evolving. Those changes rarely align with a scheduled review.

The AI broke the compliance catch-up game report by Forrester brings this mismatch into focus. Speeding up periodic compliance will only go so far. Companies need a way to keep trust current as the business changes instead of repeatedly catching up after the fact.

This blog examines what Forrester’s predictions mean for compliance teams and why keeping pace will require a move from automated compliance to autonomous trust.

Why automation solved the previous compliance problem

A year ago, most conversations about generative AI inside companies were still about pilots and proofs of concept. Fast forward to today, and agentic systems are moving into operational workflows, where they can access data, call tools, and take action with limited human input.

This shift is landing on a compliance model built for a slower environment.

Automation solved an important part of the old problem. Connected systems replaced spreadsheets, evidence could be collected on a schedule, and teams gained a clearer view of their controls. Audits that once consumed months of engineering time became easier to manage.

But most compliance software still stops after identifying the problem and creates more homework for the teams involved. When an alert arrives, someone has to understand what happened, find the person who can fix it, follow up, and verify the result. The software made discovery faster. The work required to close the gap continued to move at the pace of the team.

AI adds another complication to this problem because adoption often happens outside formal review. A team can connect an AI assistant to company data before security knows the tool exists. The tool may introduce a new model provider, create another data path, or affect commitments already made to customers. By the time compliance discovers it, other workflows may already depend on it.

Meanwhile, obligations continue to arrive through contracts, regulatory changes, and vendor updates. The amount of work grows, but the team responsible for completing it usually does not.

That gap is why we are now bringing this research to security and compliance leaders. It offers a timely view of how AI is changing governance work and whether existing operating models can keep pace.

Forrester’s 2026 Predictions: The CISO’s job is about to get harder

In its AI broke the compliance catch-up game report, published in October 2025, Forrester predicted that an agentic AI deployment would cause a public breach in 2026 and lead to employee dismissals.

The important part of the prediction is how Forrester describes the cause. It does not place responsibility on one person or one faulty action. It warns that an incident would result from a cascade of failures across the controls surrounding the agent.

Speed is one of those failure points. Forrester warns that autonomous agents may prioritize delivery speed over accuracy, particularly when interacting directly with customers. As agents gain access to internal systems and take action across connected tools, a weakness in access control or data tracking can become difficult to contain and explain.

A deployment review can record what an agent was designed and permitted to do. It cannot, by itself, show what happened during every execution as permissions, workflows, and underlying vendors changed.
This is where the prediction intersects with a problem Sprinto sees in compliance programs. A one-time assessment can document intended behavior, but it cannot keep risk and evidence current as an agent’s access, data flows, and dependencies change.

To address this, Forrester recommends a “minimum viable security” approach for agentic applications. The full report explains its AEGIS framework and the controls security leaders should consider.

The wider 2026 risk picture

Agentic AI is one of five developments covered in the report. Forrester also predicted:

• Greater government control over critical telecom infrastructure
• An EU database for known exploited vulnerabilities
• Quantum-security spending exceeding 5% of IT security budgets
• A failed attempt to revive an aging IT services provider through a cybersecurity acquisition

Each prediction comes from a different part of the security landscape. Together, they show how geopolitical decisions, infrastructure changes, emerging technology, and vendor instability can alter an organization’s risk exposure from outside its direct control.

Forrester advises CISOs to move from periodic reviews toward continuous control monitoring, a recommendation that echoes across the report’s other findings.

Download the complete report to explore the reasoning behind all five predictions and the actions Forrester recommends.

We call this gap trust debt

Forrester’s warning about a “cascade of failures” matters because a public breach is rarely where the problem begins. Smaller gaps may build for months before an incident makes them visible.

At Sprinto, we call this buildup trust debt: the difference between the obligations a company has taken on and the obligations it can prove it is meeting.

An AI tool may be approved for one purpose and later connected to more data. A vendor may change the model behind its product. A customer contract may restrict a use that nobody connects back to the workflow. Each change can appear manageable on its own, while the organization’s evidence moves further away from what is actually happening.

Trust debt often stays hidden until the company is asked to prove something specific, such as whether customer data has passed through an AI tool. That is when teams discover that the contract, approval record, and available evidence no longer describe the same reality.

By then, the team is no longer retrieving an existing answer. It is trying to reconstruct months of decisions and activity under pressure. What began as a compliance gap has become a business problem.

Keeping pace requires a new operating model

Forrester’s call for continuous control monitoring points to a broader change in how compliance work is organized. Most programs still rely on recurring reviews. When the risk changes between those reviews, teams are forced to work backward and determine what happened, which obligations were affected, and whether the existing evidence can still be trusted.

A model designed for constant change needs different rules:

• Let change set the cadence. A new vendor, AI tool, access grant, or contract requirement should trigger the relevant checks when it enters the business, not months later during a scheduled review.
• Treat resolution as part of the control. A control is not being managed simply because its failure was recorded. The work is complete only when the cause has been addressed, the result verified, and the evidence updated.
• Use people where judgment is required. People should accept risks, approve exceptions, and decide when a system needs to change. Routine coordination should not depend on someone manually moving every task forward.

This is the operating model autonomous trust requires. Under this model, compliance becomes less of a recurring catch-up exercise. The program responds as the business changes, while people remain accountable for the decisions that carry real risk.

Sprinto’s approach: from automated compliance to autonomous trust

Sprinto’s Autonomous Trust Platform puts this operating model into practice. When a change is detected, whether it is a new vendor, an access update, or an AI tool going live, the system connects it to the relevant obligations and moves the required work forward rather than simply flagging it and waiting.

Governed agents handle routine execution by requesting missing information, starting the appropriate workflow, following up with owners, and tracking the issue until its resolution is verified. People remain responsible for approvals, exceptions, and decisions involving material risk, without having to coordinate every step manually.

The result is a system where risk posture and evidence stay current as the business changes, rather than being reconstructed under pressure when someone asks for proof. That is the difference between automation that reports on drift and a system built to close it.

Forrester’s predictions show why this shift matters now. The risk environment is changing too quickly for companies to rely on periodic reviews. See how Sprinto puts autonomous trust into practice.