AI technical debt: What it is — and why it matters

One of the most dangerous drivers of data risk today isn’t even on the radar of most security teams. AI technical debt is all the more perilous for being poorly understood.

As Forcepoint noted in a recent installment of its Future Insights 2026 series, "The rapid adoption of AI platforms accelerates every shortcut: rushed integrations, outdated connectors, unpatched pipelines, and deferred architecture decisions.”

Each shortcut quietly expands the attack surface and erodes data visibility, Forcepoint wrote. AI systems ingest more data, evolve faster, and interact with more environments, which means technical debt forms quickly and often goes unnoticed until it fuels a major breach. 

Mary Carmichael, a member of the ISACA Emerging Trends Working Group, said that over time, shortcuts add up, making systems harder to change, more expensive to maintain, and more likely to fail in high-stakes moments, Carmichael said.

In 2026, this silent buildup will shape the next wave of enterprise exposure. AI technical debt is what happens when we rush AI into production and leave the hard work for later — things like cleaning up data pipelines, putting monitoring in place, or planning how a model will be updated.

Mary Carmichael

Nicolas Dupont, founder and CEO of Cyborg, said most companies are rushing to deploy applications and agentic workflows without addressing fundamental security gaps in how they’re centralizing and exposing proprietary data. “They’re building on sand,” he said.

Here’s why AI technical debt is mounting — and what you can do about it. 

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What’s causing AI technical debt?

AI technical debt commonly manifests when data pipelines are poorly governed, access is overly permissive, and workflows are agent-driven, said Diana Kelley, CISO of Noma Security. Those autonomous systems often operate with broad permissions or limited oversight as they interact with enterprise data and systems.

Because AI systems evolve continuously and ingest new data over time, technical debt can compound faster than in traditional software and will often remain hidden until a breach or other exposure occurs, Kelley said.

April Lenhard, principal product manager for cyberthreat intelligence at Qualys, said AI technical debt is mounting fast because organizations are feeling pressure to adopt AI quickly and fix any problems later — but “later” never comes. 

Unlike traditional tech debt inherent to other products, AI tech debt almost exponentially compounds. AI systems ingest exponentially more data, touch exponentially more environments, and change exponentially faster each day. Every rushed integration or skipped governance decision turns speed into exposure.

April Lenhard

The Future Insights 2026 series noted that while traditional software stacks are static, AI platforms never sit still. New data sources, shifting access patterns, and fast-changing compliance requirements create constant pressure to ship now and fix later, Forcepoint wrote. That pressure compounds debt across discovery, classification, and governance workflows, leaving behind fragile connectors, monolithic components, and inconsistent coverage.

The result is a widening set of blind spots, the report continued. Sensitive data goes unclassified, permissions drift out of alignment, and misconfigurations persist for years. As debt accumulates, visibility weakens and the likelihood of unnoticed data exposure rises. “Organizations entering 2026 will need to recognize and address this silent risk before it becomes the source of their next breach,” wrote Forcepoint

Why the attack surface is expanding

Most enterprise data risk today is caused by infrastructure misconfigurations rather than malware, said Iftach Ian Amit, founder and CEO of Gomboc.ai. When AI generates infrastructure as code (IaC) that defines storage access, encryption settings, network paths, or identity relationships incorrectly, it can expose sensitive data without triggering traditional security alerts, he said. “The infrastructure appears functional, but the data is accessible in ways the organization never intended.” 

AI dramatically accelerates infrastructure change, often faster than teams can review or reason about it. If an organization already has weak IaC patterns or legacy misconfigurations, AI tends to replicate and amplify those patterns across environments. What was once a small set of risky configurations can quickly spread across multiple repositories and cloud accounts.

Iftach Ian Amit

Melody (MJ) Kaufmann, an author and instructor at O’Reilly Media, said AI technical debt can expand an organization’s attack surface and create blind spots. 

AI technical debt amplifies the attack surface by extending legacy identity and permission flaws into always-on, machine-driven access, and it hides data risk by shifting access and reuse into automated systems that lack clear visibility or ownership.

Melody (MJ) Kaufmann

AI technical debt expands the attack surface by layering new systems on top of existing technical debt without fixing the underlying weaknesses, said ISACA’s Carmichael. “It rarely introduces brand-new risks. Instead, it amplifies the ones already there,” she said.

Cyborg’s Dupont said AI vendors are telling customers that AI will show its value only when it has access to all of the organization’s data. “To get ROI from enterprise AI, you need proprietary data,” he said. “So organizations are taking data that was previously siloed across finance, HR, engineering, and call centers and centralizing it into vector databases. That creates a single point of failure.”

There’s a widespread mischaracterization that vector embeddings are somehow anonymized or encrypted. They’re not. They’re mathematical representations that can be inverted. Security researchers have repeatedly demonstrated that you can reconstruct original data from embeddings with high accuracy. This isn’t theoretical. It’s a documented attack vector.

Nicolas Dupont

Dupont said most organizations think their AI infrastructure is secure because they have encryption at rest and in transit, but vector embeddings typically aren’t encrypted during use. “Most vector databases delegate security to the OS layer, which means any access to the database exposes the data,” he said.

Why traditional security tools are ineffective

The Future Insights series also noted that traditional security tools such as firewalls, SIEMs, and endpoint protection are not designed to detect the nuanced risks introduced by AI technical debt in data discovery and classification because they lack visibility into real-time permission changes, schema evolution, and open or misconfigured databases.

O’Reilly’s Kaufmann said traditional security tools overlook AI technical debt because they focus on monitoring infrastructure events, rather than how data is continuously accessed and reused by machines.

Firewalls, SIEMs, and endpoint tools operate after infrastructure is already deployed, said Gomboc.ai’s Amit. “They do not evaluate whether infrastructure should exist in the first place or whether it aligns with security and data governance intent,” he said.

AI technical debt lives in IaC long before runtime tools ever see traffic or behavior. By the time traditional tools detect an issue, the misconfiguration has often already exposed data.

Iftach Ian Amit

Firewalls, SIEMs, and endpoint tools are built to watch infrastructure, but they’re not geared to the fluidity of fast-changing data inside AI systems, said Qualys’s Lenhard. “Without investing in tools built specifically to monitor AI environments, organizations will lose visibility into broken governance or model changes until it’s too late,” she said.

ISACA’s Carmichael said AI technical debt introduces risk through data quality, lineage, and model behavior, areas that traditional security tools do not see. Model drift, poisoned training data, and undocumented transformations can impact outputs without triggering security alerts, she said. “In short, AI shifts risk to the data and decision layer, while most security tools remain focused on networks and the endpoint layer,” she said. 

AI technical debt differs from traditional technical debt in one critical way, Carmichael said: It hides.

Traditional technical debt usually surfaces through outages, bugs, or performance issues. AI technical debt can hide behind ‘plausible’ model outputs, quietly building risk at the data and decision layer. Because of this, AI technical debt is not a technical concern.

Mary Carmichael

Carmichael stressed that AI technical debt creates enterprise risk across the areas of security, privacy, compliance, and business integrity. “Managing it requires a new, tool-driven approach focused on data visibility, lineage, access control, and continuous monitoring across the AI lifecycle,” she said.