AI Is Everywhere, But Security Teams Are Still Using Yesterday’s Skills and Tools

Artificial intelligence is now embedded across products, workflows, and customer experiences, yet many organizations are still trying to secure these systems with legacy methods. As AI adoption accelerates, the gap between modern AI risks and traditional cybersecurity capabilities is widening at an alarming pace.

A new benchmark study of 300 CISOs and senior security leaders in the United States reveals that most organizations lack the tools, skills, and processes required to defend AI infrastructure effectively. For business leaders and technical teams, this disconnect represents both a strategic risk and a pressing opportunity.

Key Takeaways

• Most security teams are trying to secure AI with traditional tools that were not designed for machine learning models or data pipelines.
• Skill gaps in AI security and adversarial testing are leaving organizations exposed to sophisticated attacks and misconfigurations.
• AI infrastructure is often deployed faster than it is secured, creating blind spots across data, models, and integrations.
• CISOs must evolve security strategies to include AI-specific risk assessments, tooling, and cross-functional collaboration.

The New Reality: AI Everywhere in the Enterprise

AI is no longer a niche experiment confined to innovation labs. It is deeply integrated into core business functions such as fraud detection, customer support, demand forecasting, marketing automation, and operational analytics. Organizations are deploying:

• Machine learning models embedded into web applications and APIs
• Large language models (LLMs) for content generation and code assistance
• Recommendation engines driving personalized user experiences
• Predictive models feeding into financial and operational decisions

Each of these use cases introduces new attack surfaces. Unlike traditional applications, AI systems rely on complex data pipelines, third-party models, and continuous retraining cycles. This complexity makes it harder for security teams to understand how and where to apply controls.

Why Traditional Security Models Are Not Enough

Legacy security tooling focuses on protecting networks, endpoints, web applications, and static code. AI environments add additional layers:

• Training data that can be poisoned or exfiltrated
• Models that can be stolen, reverse engineered, or manipulated
• Inference APIs that can be abused for prompt injection, data leakage, or model manipulation
• MLOps pipelines that introduce misconfigurations and supply chain risks

Without AI-specific security controls, organizations are effectively blind to many of these threats. Standard vulnerability scans and penetration tests rarely cover adversarial ML scenarios or model abuse cases.

What the Benchmark Study Reveals About CISO Challenges

The AI and Adversarial Testing Benchmark Report 2026, based on a survey of 300 US-based CISOs and senior security leaders, highlights a consistent theme: security programs have not kept pace with AI adoption.

Skills Gaps in AI and Adversarial Security

One of the most pressing problems is the scarcity of specialized skills. While many security professionals understand application, cloud, and network security, far fewer are equipped to deal with:

• Adversarial machine learning and model manipulation
• Prompt injection and model output manipulation for LLMs
• Data poisoning attacks during model training
• Securing MLOps pipelines and AI supply chains

As a result, even mature security teams often lack the expertise to evaluate whether their AI systems are resilient under real-world adversarial conditions.

“AI is being deployed at scale with the assumption that existing security practices are enough—when in reality, many AI-specific threats are going untested and unmitigated.”

Tools Built for Yesterday’s Threats

The study underscores that most organizations are attempting to secure AI infrastructure using traditional tools—WAFs, SIEMs, static analysis, and generic penetration testing. While these remain essential, they do not fully address AI-centric risks.

For example, a conventional penetration test might validate access controls around an AI-based API, but it will rarely explore:

• Whether the model can be tricked into leaking sensitive training data
• How robust the model is against adversarial inputs
• Whether the model’s behavior can be subtly influenced over time

This gap leaves organizations with a false sense of security. Systems appear “secure” by traditional measures but remain exposed to AI-specific attacks.

Where AI Infrastructure Is Most Vulnerable

As AI moves from pilot projects into production, its infrastructure stack becomes increasingly complex. The report highlights several areas where vulnerabilities commonly emerge.

Data Pipelines: The Foundation with Hidden Risk

AI models are only as trustworthy as the data used to train and update them. However, many organizations lack end-to-end visibility and controls across:

• Data ingestion from internal and external sources
• Data labeling and transformation workflows
• Storage of sensitive training datasets
• Access control and governance across data lakes and warehouses

Threats such as data poisoning, unauthorized data access, and subtle manipulation of training sets can compromise AI integrity without triggering traditional security alerts.

Models and APIs: New Front Doors for Attackers

Once models are deployed, they are typically exposed through APIs that power applications, dashboards, and decision engines. These interfaces can be abused in several ways:

• Prompt injection and jailbreak attempts against LLM-powered services
• Model extraction, where attackers query models to replicate their behavior
• Abuse of inference endpoints to derive sensitive information about training data
• Business logic manipulation via crafted inputs that trigger unintended outputs

Standard API security measures—rate limiting, authentication, and input validation—remain important but must be augmented with AI-specific security testing and monitoring.

Bridging the Gap: How CISOs Can Modernize AI Security

To align security capabilities with AI adoption, CISOs and technology leaders need a structured approach that goes beyond incremental adjustments. The survey results point toward several practical steps.

1. Build AI Security Expertise Inside the Organization

Investing in skills is critical. This may include:

• Training security engineers on AI fundamentals and adversarial ML concepts
• Embedding security specialists within data science and MLOps teams
• Partnering with external experts for initial assessments and knowledge transfer

Cross-functional collaboration between security, data, and development teams is essential to understand how AI is built, deployed, and consumed across the business.

2. Integrate AI-Specific Threat Modeling and Testing

Traditional threat modeling must be extended to consider AI-specific attack vectors. This includes:

• Mapping how data flows into, through, and out of AI systems
• Identifying where training data can be influenced or intercepted
• Assessing how models could be abused or reverse engineered
• Testing models with adversarial inputs and red-teaming exercises

Security teams should adopt or evaluate platforms and tools that support adversarial testing and continuous validation of AI systems, rather than relying solely on point-in-time penetration tests.

3. Embed Security into MLOps and Development Pipelines

Just as DevSecOps integrated security into software delivery, organizations need a similar mindset for AI workflows. This can involve:

• Implementing security checks within data and model pipelines
• Versioning and monitoring models for unexpected behavior changes
• Using policy-based controls for deploying and rolling back models
• Automating checks for configuration drift and access misconfigurations

By treating AI artifacts—datasets, models, prompts, and configurations—as first-class assets in the development lifecycle, security becomes an integral part of how AI is shipped and maintained.

Implications for Business Owners and Technical Leaders

For business owners, AI promises competitive advantage, efficiency gains, and new revenue opportunities. However, if deployed without adequate security, it can also introduce:

• Regulatory and compliance exposure through mishandled data
• Brand damage from biased, incorrect, or manipulated AI outputs
• Operational disruptions if critical models are compromised

Technical leaders—CISOs, CTOs, and heads of engineering—must ensure that AI initiatives are aligned with a realistic security strategy. This involves budgeting for security tooling, allocating time within delivery roadmaps, and setting clear risk thresholds for AI usage.

AI Security as a Business Enabler

When done correctly, AI security is not just a defensive measure; it becomes a differentiator. Organizations that can prove their AI systems are robust, transparent, and well-governed will be better positioned to win trust from customers, partners, and regulators.

For web applications, customer portals, and data-driven platforms, this means combining strong cybersecurity practices with secure custom web development that accounts for AI-specific risks from the start.

Conclusion

The AI and Adversarial Testing Benchmark Report 2026 makes one thing clear: while AI is now core to business operations, security programs are still catching up. Most organizations are defending tomorrow’s AI-powered environments with yesterday’s tools and skill sets.

To close this gap, CISOs and technology leaders must modernize their approach—building AI security expertise, integrating adversarial testing, and embedding security into MLOps and development pipelines. Organizations that act now will be better equipped to harness AI safely and confidently, while those that delay risk exposing their most critical systems and data to evolving threats.