Pentera 2026 Report: AI Security Gaps and EU AI Act Compliance for CISOs

The Urgent Need for Modern AI Security Tools

As artificial intelligence adoption accelerates across industries, Chief Information Security Officers (CISOs) face unprecedented challenges in securing these complex systems. The 2026 AI and Adversarial Testing Benchmark Report from Pentera, surveying 300 US CISOs and senior security leaders, reveals a critical disconnect: organizations are deploying AI technologies at scale while relying on outdated security tools and practices. This gap creates significant vulnerabilities just as the EU AI Act's security requirements become enforceable. With high-risk AI system obligations applying from 2 August 2026 under Regulation (EU) 2024/1689, CISOs must urgently update their skills and tools to address AI-specific threats while meeting regulatory mandates for robustness, transparency, and risk management.

Pentera Study: Key Findings on AI Security Gaps

Pentera's 2026 benchmark report provides quantitative evidence of systemic weaknesses in AI security preparedness. The study methodology involved structured surveys and interviews with security leaders across multiple sectors, focusing on their ability to defend against AI-specific attacks. Three critical findings emerged:

• Tool Deficiency: The majority of security leaders reported lacking adequate tools to defend AI systems against modern threats. Traditional security solutions designed for conventional IT infrastructure fail to address the unique attack surfaces of machine learning models and AI pipelines.
• Skills Shortage: Organizations face critical gaps in personnel with expertise in both cybersecurity and AI/ML technologies. This dual-domain knowledge is essential for implementing effective AI security controls.
• Outdated Practices: Many organizations continue applying legacy security frameworks to AI systems, creating protection gaps against adversarial attacks, data poisoning, model theft, and other AI-specific threats.

These findings align with broader industry concerns about AI governance gaps, as highlighted in our analysis of AI talent departures and AI safety incidents. The Pentera report underscores that technical vulnerabilities directly translate to compliance risks under emerging AI regulations.

AI Security Challenges: Adversarial Attacks and Beyond

AI systems introduce novel security challenges that traditional cybersecurity approaches cannot adequately address. The Pentera report highlights several specific threat vectors that CISOs must understand:

Adversarial Attacks

These involve subtle manipulations of input data designed to cause AI models to make incorrect predictions or classifications. For example, adding imperceptible noise to an image might cause an autonomous vehicle's object detection system to misidentify a stop sign. Adversarial attacks exploit the mathematical properties of machine learning models and require specialized detection and mitigation techniques.

Data Poisoning

During the training phase, attackers can inject malicious data into training datasets to compromise model behavior. A poisoned dataset might cause a fraud detection system to learn incorrect patterns or a content moderation system to develop biased classifications. Data poisoning attacks are particularly concerning because they can persist undetected through the entire AI lifecycle.

Model Inversion and Extraction

Attackers can sometimes reverse-engineer AI models through API queries to extract proprietary algorithms or training data. Model extraction attacks threaten intellectual property and can expose sensitive information contained in training datasets.

These threats require security controls specifically designed for AI systems. As discussed in our analysis of Microsoft Copilot security flaws, even well-established technology providers struggle with AI security implementation. The NIST AI Risk Management Framework (AI RMF 1.0), published in January 2023, provides voluntary guidance for addressing these risks through its four core functions: Govern, Map, Measure, and Manage.

Aligning Security Practices with EU AI Act Requirements

The EU AI Act establishes legally binding security requirements for AI systems, particularly those classified as high-risk. The Pentera report's findings directly relate to several key provisions that organizations must address for compliance:

Robustness and Accuracy (Article 15)

For high-risk AI systems, Article 15 requires that they "achieve an appropriate level of accuracy, robustness and cybersecurity throughout their lifecycle." This includes resilience against errors, faults, inconsistencies, and—critically—unauthorized attempts to alter their use or performance. The Pentera findings about inadequate tools for defending against adversarial attacks directly challenge organizations' ability to meet these robustness requirements. High-risk AI systems, including those used in recruitment and employment under Annex III area 4, must implement appropriate technical solutions to ensure security.

Risk Management System (Article 9)

Providers of high-risk AI systems must establish, implement, document, and maintain a risk management system throughout the AI lifecycle. This system must identify and analyze known and foreseeable risks, estimate and evaluate risks that may emerge, and adopt appropriate risk management measures. The skills gaps identified in the Pentera report undermine organizations' capacity to implement effective risk management systems as required by the regulation.

Transparency and Human Oversight (Articles 13-14)

The AI Act requires high-risk AI systems to be designed and developed with capabilities enabling human oversight, and to provide transparency regarding their capabilities and limitations. Security vulnerabilities that allow unauthorized model manipulation directly conflict with these transparency and oversight requirements.

With obligations for high-risk AI systems applying from 2 August 2026 (and for those embedded in regulated products like medical devices until 2 August 2027), organizations have limited time to address these gaps. Penalties for non-compliance can reach EUR 15 million or 3% of global annual turnover for violations of high-risk AI requirements. Our EU AI Act compliance roadmap guide provides detailed implementation strategies for meeting these security mandates.

Recommendations for CISOs: Bridging the AI Security Gap

Based on the Pentera findings and EU AI Act requirements, CISOs should implement several practical measures to strengthen AI security and ensure compliance:

Implement Adversarial Testing Programs

Regular adversarial testing should become a standard component of AI system development and deployment. This involves systematically probing AI models with crafted inputs to identify vulnerabilities before attackers can exploit them. Adversarial testing aligns with both security best practices and the EU AI Act's requirements for robustness validation. Organizations should establish testing protocols that cover the full range of potential attacks, including evasion, poisoning, and extraction attempts.

Adopt AI-Specific Security Tools

CISOs must move beyond traditional security solutions and implement tools specifically designed for AI protection. Several vendors offer specialized solutions:

• Holistic AI: Provides a governance platform with security modules for risk assessment, bias detection, and adversarial robustness testing. Their tools help organizations implement controls aligned with the EU AI Act and ISO/IEC 42001 standards.
• Vanta: While primarily known for SOC 2 and ISO 27001 compliance automation, Vanta has expanded into AI governance with features for risk assessment and control monitoring. Contact vendor for pricing.

For a comprehensive comparison of available solutions, see our analysis of AI governance platforms. When evaluating tools, CISOs should prioritize capabilities for continuous monitoring, vulnerability detection, and compliance reporting.

Develop Cross-Domain Expertise

Addressing the skills gap requires both hiring specialized talent and upskilling existing security teams. CISOs should invest in training programs that combine AI/ML fundamentals with security principles. Collaboration between security, data science, and compliance teams is essential for implementing effective AI governance. The EU AI Office's recruitment of scientific experts highlights the growing demand for this cross-domain expertise at regulatory levels as well.

Integrate Security into AI Governance Frameworks

AI security should not operate in isolation but rather integrate with broader governance frameworks. Organizations can leverage established standards like ISO/IEC 42001 (published December 2023) for certifiable AI management systems, or implement the NIST AI RMF's voluntary framework. These approaches help ensure security considerations are embedded throughout the AI lifecycle, from design to decommissioning. Our complete guide to AI governance provides additional context for integrating security with other governance domains.

Leverage Compliance Intelligence Platforms

Continuous monitoring of regulatory requirements is essential as AI regulations evolve. Platforms like AIGovHub provide intelligence on changing mandates across jurisdictions, helping organizations stay ahead of compliance deadlines. For CISOs managing AI security, such tools offer:

• Real-time alerts on regulatory updates, including changes to EU AI Act implementation timelines
• Vendor comparison capabilities to evaluate AI security solutions
• Compliance checklists tailored to specific AI use cases and risk levels
• Integration with existing security and governance workflows

By combining technical security measures with regulatory intelligence, organizations can build comprehensive AI protection strategies that address both threat prevention and compliance requirements.

Key Takeaways and Actionable Steps

The Pentera 2026 report serves as a wake-up call for security leaders navigating the intersection of AI adoption and regulatory compliance. Key insights include:

• Most organizations lack adequate tools and skills to secure AI systems against modern threats
• These security gaps create significant compliance risks under the EU AI Act, particularly for high-risk AI systems with obligations applying from 2 August 2026
• Adversarial attacks, data poisoning, and model extraction require specialized security controls beyond traditional cybersecurity approaches
• Integration of security with broader AI governance frameworks is essential for both protection and compliance

To address these challenges, CISOs should take immediate action:

1. Conduct an AI security assessment to identify vulnerabilities in existing systems and gaps in current protection measures
2. Implement adversarial testing programs as part of AI development and deployment workflows
3. Evaluate and adopt AI-specific security tools from vendors like Holistic AI or Vanta to address identified protection gaps
4. Develop cross-functional teams with expertise in both cybersecurity and AI/ML technologies
5. Monitor regulatory developments using compliance intelligence platforms to ensure ongoing alignment with EU AI Act and other mandates

Some links in this article are affiliate links. See our disclosure policy.

As AI systems become increasingly critical to business operations, their security cannot be an afterthought. The convergence of sophisticated threats and stringent regulations requires a proactive approach that integrates technical protection with governance and compliance. By addressing the gaps highlighted in the Pentera report, organizations can build resilient AI systems that not only withstand attacks but also meet evolving regulatory expectations.

Ready to strengthen your AI security and compliance posture? Try AIGovHub's AI governance compliance checker to assess your current alignment with EU AI Act requirements and identify priority areas for improvement. Our platform provides continuous monitoring of regulatory changes, vendor comparisons, and implementation guidance to help CISOs navigate the complex landscape of AI security and compliance.