A Closer Look at the Vulnerabilities Hidden Inside AI Workflows

AI workflows face hidden risks: data poisoning, model extraction, adversarial attacks & shadow AI. Secure with monitoring, governance & input validation.

AI has moved into the center of modern business so quickly that many teams barely had time to consider where the weak points might be forming. Models now influence product suggestions, customer responses, internal analytics, and many of the decisions that guide day-to-day operations. Yet the safeguards that usually accompany major software changes did not keep pace. As adoption accelerates, the Key pillars of AI Security have shifted from a theoretical concept to something organizations use to steady themselves. The more influence these models gain, the more visible even a small behavior change becomes.

AI Systems Are Growing Faster Than Security Controls Can Keep Up

Deloitte’s 2024 State of Generative AI in the Enterprise study noted that 73 percent of organizations with high generative AI maturity are adopting the technology fast or very fast, even while these same teams highlight risk management and responsible use as ongoing concerns. This tension reflects the nature of AI systems. A model is never a single element. It sits on layers of training data, preprocessing steps, open-source libraries, third-party components, and cloud infrastructure. Any of these layers can introduce uncertainty.

Attackers have taken advantage of this. A model that shapes automated scoring or customer journeys can be more attractive than a compromised device. If someone influences how a model learns or responds, the effect may spread into other systems long before anyone realizes something is off.

The New Attack Surface Built by Modern AI Workflows

Training remains one of the earliest vulnerable points. Research from the University of Maryland, commonly known as the Poison Frogs work, showed that a single poisoned example can alter a model’s future behavior. It does not take hundreds of manipulated images. One targeted sample can be enough when organizations rely on open datasets or external sources.

Once training ends, new risks appear. Academic papers published in 2023 and 2024 showed that repeated and structured querying can reveal hints about a model’s internal reasoning or its training distribution. Model extraction attacks build from the same idea and allow an attacker to approximate a system simply by observing its outputs.

Inference introduces a different set of problems. Studies on adversarial robustness consistently show that tiny input adjustments, almost invisible to a human, can lead certain vision models to misclassify objects entirely. The size of the accuracy drop varies across studies, but the trend is clear. Prompt manipulation has a similar effect but a different mechanism. Universities, including Stanford, have examined how crafted instructions can shift a model’s responses without touching its underlying code.

The Underestimated Weak Links Hidden Inside AI Pipelines

In practice, many failures originate in the workflow around the model rather than in the model itself. Shadow AI is a good example. Microsoft’s Work Trend Index reported that 71 percent of employees have used unapproved AI tools at least once. A separate security-focused survey found that 69 percent of security leaders suspect or have confirmed shadow AI inside their organizations. These cases usually begin with someone trying to save time. The risk appears when data moves into tools no one has reviewed.

Dependencies increase this uncertainty. AI developers often pull from community-maintained libraries and pretrained models. Recent supply chain issues involving compromised Python packages and altered weights show how quietly risk can enter a pipeline long before deployment. Even experienced teams struggle to verify every dependency.

Cloud infrastructure contributes its own complications. Public incident summaries from 2023 and 2024 describe situations where misconfigured storage or overly broad access permissions exposed training artifacts or model weights. These cases rarely result from careless behavior. They reflect the difficulty of managing several layers of cloud services at once.

Data lineage remains another unresolved challenge. Many organizations cannot fully trace the origin or transformation history of the data that shaped their models. Without that trail, subtle manipulation becomes much harder to detect.

The Key Pillars of AI Security That Organizations Are Prioritizing in 2025

To address these challenges, organizations are aligning their practices with frameworks such as the NIST AI Risk Management Framework, ISO and IEC 42001 and ENISA’s growing body of work on AI related threats. These standards were developed in response to early deployments where governance and data handling failures were often more common than algorithmic issues.

Model and dataset integrity usually come first. Secure storage, hashing, dataset versioning, and watermarking help create a traceable record when unexpected behavior appears. Securing development and deployment follows close behind. Teams rely on adversarial testing, input validation, and controlled endpoints to keep workflows predictable.

Monitoring has become increasingly important. AI systems adjust as they are used, and early signs of drift or irregular patterns often show up in logs before they show up in user experience. Governance supports this work by clearly defining roles, recording changes, and limiting access based on responsibility. Infrastructure security underpins these measures through isolated environments and hardened configurations. Incident response plans are also being updated so teams can address AI-specific failures with the same discipline applied to traditional outages.