Scaling AI across enterprises is moving faster than most security teams can adapt. IBM’s 2025 X-Force Threat Intelligence Index reported an 84% rise in phishing emails delivering infostealers in 2024, often powered by generative AI to mimic human behavior and scale attacks.

AI is no longer confined to research or controlled environments. Large language models (LLM), image generators, and API-integrated tools are now embedded in workflows, often with limited oversight. Security teams are being pulled in late — or not at all — while developers and business users race to integrate new capabilities.

AI security risks are no longer theoretical. Model inputs can be manipulated. Inference APIs are exposed. Data pipelines are mishandled. Traditional security controls, built for deterministic software, are not designed to protect systems that learn and adapt.

Scaling AI without addressing these risks introduces structural blind spots attackers are already exploiting. Speed, without alignment to security principles, is driving exposure far faster than most enterprises can respond.

The New AI Attack Surface

Artificial intelligence expands the typical error-prone software environment with novel vulnerabilities across various model types such as LLMs, generative systems, and vision models. One prominent concern is prompt injection, where attackers embed malicious instructions into inputs that mislead models into performing unintended actions. OWASP’s 2025 Top?10 for LLM Applications places prompt injection at the very top, driven by research showing that 56% of tested injection attempts succeeded across 36 models. Model inversion and extraction attacks add another layer of risk by allowing adversaries to recover sensitive training data or replicate proprietary model behavior.

Adversarial inputs then manipulate model outputs causing incorrect decisions in tasks like fraud detection or image recognition. Many deployed AI systems lack rigorous alignment testing or red-teaming. OWASP warns that most LLM applications go live with no advanced adversarial testing. Without proactive security evaluations, deployment becomes a pipeline of trust-blind automation.

As new AI services engage in everyday processes — customer support, code suggestions, document parsing — they introduce interwoven touchpoints where attackers can exploit unseen failure modes. Expedited scaling often prioritizes feature delivery over security, leaving prompt vectors, API endpoints, and control flows exposed, precisely where adversaries can infiltrate next-generation systems.

Shadow AI and Unsecured Integrations

Shadow AI is manifesting as a significant cybersecurity threat: employees often use generative AI tools without IT approval, embedding them into workflows with serious consequences. In a 2025 KPMG University of?Melbourne survey involving 48,000 workers, 57% admitted concealing AI usage from employers, while 48% uploaded company data into public AI tools. Separate studies reveal that 68% of generative AI users access models through personal accounts, and 57% have sent sensitive corporate information into unsanctioned AI systems. Data funnels into third-party models, and inference APIs often skip standard authentication, creating a chase-the-breach scenario for security teams who lose sight of these hidden data flows.

In many cases, traditional defenses like DLP, firewalls, or SIEM systems offer no visibility into these AI-integrated workflows. The volume of leaked prompts to AI rose over 30-fold in a year, according to research on shadow AI behavior.

With so much happening outside official channels, enterprises face a double bind in the form of unvetted APIs plus rampant data exposure. Revealing patchwork governance, few organisations maintain policies around AI usage, and even when sanctioned tools are available, employees bypass them. The result is a sprawling ecosystem of undocumented AI services, each one a potential entry point for data leakage or model compromise.

Unvetted Components and AI-BOM Gaps Are Weakening AI Supply Chains

Many AI models rely on a mix of pre-trained components and open-source code whose origins are unclear. A 2025 report by ReversingLabs found that supply chain attacks increasingly target AI and machine learning ecosystems, with malicious code insertion into open-source packages and commercial binaries on the rise. Developers frequently pull from “model zoos” like HuggingFace or GitHub without verifying the origins of components, a growing concern emphasized by ExtraHop, which predicts escalating attacks on both open-source and proprietary AI supply chains in 2025.

Third-party AI APIs further compound risk. A compromised API could deliver tainted models or steal inference data. Without standardized software bills of materials (SBOMs) for models and datasets, security teams are left blind. While decades-old software relies increasingly on SBOMs, AI-specific extensions (AI-BOMs) that list datasets, frameworks, and algorithms are only now being proposed. Lack of transparency in model supply chains means vulnerabilities in dependencies can be weaponized long before deployment.

Rushed integration of these components into internal systems means hidden dependencies may carry unrecognized flaws. If no SBOM exists, auditing becomes impossible, and organizations can’t trace what could be backdoored, out-of-date, or non-compliant with licensing and data standards.

Data Exposure and IP Leakage

Using sensitive data to train or test AI carries significant risk when inputs are unmasked or unrestricted. Sensitive training datasets may include personal information, proprietary algorithms, or internal code. One high-profile incident involved Samsung engineers who mistakenly uploaded source code and hardware-related meeting notes into ChatGPT, an occurrence reported three times in April 2023. Once exposed, that data becomes part of the model’s learning and potentially recoverable by others.

In 2025, organizations are still lacking controls that mask or tokenize sensitive fields before feeding them into models. AI security risks escalate when Personally Identifiable Information (PII), Protected Health Information (PHI), or company Intellectual Property (IP) enters systems without encryption or strict access policies. Without proper data governance, models can inadvertently memorize and leak training data, even via prompt extraction attacks.

Model inversion risks compound this problem: attackers recover training data from models, revealing private details or secrets that were never intended for public exposure. When AI is scaled quickly without data classification steps and masking pipelines, what was once hidden becomes voluntarily surrendered. Structured oversight is needed to detect, isolate, and sanitize data before it enters any training or inference stage.

Compliance and Governance Gaps

AI systems often drift outside the boundaries of established compliance frameworks like HIPAA, GDPR, and PCI?DSS. For example, personal data used in automated decision-making remains a blind spot under GDPR unless a Data Protection Impact Assessment (DPIA) is performed. The EU’s AI Act, effective since August 2024, mandates risk-based classification and transparency for general-purpose models, but detailed guidance like a voluntary Code of Practice is still pending until late 2025. In the U.S., frameworks such as NIST’s AI RMF and SOC?2 overlap only partially with compliance mandates; most organizations lack formal AI risk programs, and only a few feel prepared to manage AI-related risks.

Enterprises need AI-specific risk assessments, governance structures, and audit processes. These should include classification of AI systems, standardized evaluation of privacy/security risks, and documented external audits or third-party reviews. Without AI-tailored frameworks, organizations rely on outdated controls that miss threats unique to model behavior and data lineage. Governance must evolve in parallel with AI scaling to close these compliance and oversight gaps.

Recommendations and Cybersecurity Safeguards

• Build security reviews into development pipelines. Introduce threat modeling, adversarial testing, and external audits before deployment, flagging model-specific threats early and often.
• Restrict AI usage to vetted enterprise tools. Enforce authentication, access control, and segregation of duties where AI systems handle sensitive data.
• Monitor for shadow AI. Use network telemetry, DLP policies, and prompt scanning to detect unsanctioned AI workflows before they expose data.
• Adopt AI/ML Bill of Materials (AI-BOM). Maintain inventory of model components, dependencies, datasets, licenses, and version history, making it easier to assess vulnerabilities across the supply chain.
• Red-team your AI models. Conduct regular adversarial and alignment testing to probe for prompt injection, model inversion, data leaks, and compliance exposures.

These steps don’t require sacrificing speed, but they do require structured integration into tech operations. Organizations that scale AI securely validate innovation and resilience at every stage. When applied consistently, these practices convert fast scaling into defensible, risk-managed AI deployment.

From Exposure to Control: Building a Security Foundation for AI

Every risk outlined so far, whether it’s unmonitored model behavior, hidden API misuse, or policy breakdowns, is rooted in a lack of control over how AI is introduced and integrated. Tools are adopted without scrutiny. Data flows are created without inspection. Security and compliance functions are pulled in too late to make a difference.

Saner Platform helps shift that dynamic. It offers system-level visibility and control mechanisms needed to prevent AI from becoming another blind spot. Endpoint monitoring reveals where unauthorized AI tools or shadow integrations are being used. Misconfiguration and vulnerability assessments highlight exposure in systems supporting inference, storage, and API operations. Software bills of materials (SBOMs) extend to AI-specific components, providing a structured inventory of models, datasets, and dependencies. Compliance modules map AI usage against existing controls such as HIPAA, GDPR, and PCI DSS, while policy enforcement capabilities help close governance gaps before AI expands across environments.

Securing AI is not about hardening the model alone. It requires a platform-level approach that traces every step, who’s using AI, what it’s connected to, and where trust assumptions are failing. Saner Platform delivers that foundation before adoption scales into exposure.

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