Artificial intelligence now powers everyday business decisions, customer experiences and internal workflows. Yet security practices have not kept pace. The ai security gap is the widening distance between rapid AI adoption and the governance, controls and monitoring required to protect data and outputs. Closing this gap calls for disciplined AI governance, strong technical safeguards and a culture of continuous oversight that treats AI as a living system and a core pillar of AI in cybersecurity.
What Is the AI Security Gap?
The ai security gap is the mismatch between how quickly organizations deploy AI and how effectively they secure it. It appears as unprotected machine learning pipelines, uncertain ownership for models and prompts and limited visibility into model behavior and supply chain dependencies. Analysts report accelerating AI use alongside rising exposure, with many security leaders piloting or deploying AI without formal model governance, third-party risk controls, or clear data handling policies.
Several drivers widen this gap: rapid experimentation without security review, shadow AI initiated by business units, complex model supply chains that mix proprietary and open-source components, scarce AI security expertise and fragmented tools that create blind spots across data, models and infrastructure. These conditions elevate AI vulnerabilities and underscore the need for consistent AI governance embedded within AI in cybersecurity programs.
Key Risks You Need to Manage
AI introduces new attack surfaces while inheriting traditional risks. Common threats include:
- Prompt injection and jailbreaking of generative models
- Data poisoning in training datasets and feature stores
- Model theft via API scraping and insecure endpoints
- Adversarial inputs that cause misclassification or harmful outputs
- Insecure MLOps pipelines that leak secrets or allow unauthorized model changes
Consequences span industries. In financial services, manipulated models can fuel fraud or mispricing. In healthcare, tainted data degrades diagnostic accuracy. Retail and customer care bots have been coerced into revealing sensitive information via crafted prompts. These events erode trust, create regulatory exposure, and increase incident response costs.
Privacy is central to AI risk. Models trained on personal or sensitive data can memorize and reveal it. Weak data minimization, unclear consent and insufficient de-identification heighten violations. When sensitive data flows to third-party model providers without strong contractual controls, privacy gaps quickly become security issues that widen the ai security gap. Effective AI governance should define data retention, vendor obligations and continuous validation to reduce leakage risks.
How to Close the AI Security Gap
Effective AI security spans the full lifecycle: data collection, training, deployment, and ongoing monitoring.
- Establish governance: Create an AI asset inventory, map data lineage and assign clear ownership for models, prompts and datasets. Integrate AI risks into enterprise risk registers. Make AI governance a cross-functional mandate, not a side project.
- Control access: Enforce role-based access for datasets, feature stores and model registries. Use secrets management for keys and tokens. Tie controls into AI in cybersecurity monitoring for end-to-end visibility.
- Harden endpoints: Apply input validation, output filtering and rate limiting. Use retrieval augmentation and allow lists to constrain model behavior. Isolate high-risk workloads to reduce exposure to AI vulnerabilities.
- Monitor and test: Track anomalous usage (token spikes, query patterns, cross-tenant access). Conduct red teaming for prompt injection, data exfiltration and adversarial examples. Treat testing as continuous to prevent the ai security gap from reopening after changes.
- Audit and align: Perform model risk assessments pre-deployment and on a defined cadence. Validate third-party assurances and align with NIST AI RMF, NIST CSF and SOC 2.
IP Pathways SPaaS: AI Assessment Add-On
As AI usage grows and AI-enabled cyberattacks surge — industry sources report a 47% global increase in 2025 — visibility and governance are essential. To help customers manage this risk, IP Pathways offers an AI Assessment add-on to our Security Posture as a Service (SPaaS). Our team identifies AI vulnerabilities, prioritizes remediation and helps you shrink the ai security gap with measurable outcomes.
What the AI Assessment delivers:
- Discovery of AI usage, including shadow AI across business units
- Gap analysis of governance, privacy and security controls
- Risk scoring for models, data flows and third-party providers
- Actionable roadmap with prioritized remediation steps
- Alignment guidance for NIST AI RMF, NIST CSF and SOC 2
Why now:
- 78% of CISOs report AI-powered threats significantly impact their organizations
- Rising regulatory pressure requires demonstrable visibility and control
- Faster adoption than governance increases data, compliance and security risk
Explore our perspective on AI security risk and the Tenax Assessment on our blog: Read the post.
Ready to get proactive protection? Contact us for details.
