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HiddenLayer Releases the 2026 AI Threat Landscape Report, Spotlighting the Rise of Agentic AI and the Expanding Attack Surface of Autonomous Systems
HiddenLayer secures agentic, generative, and predictAutonomous agents now account for more than 1 in 8 reported AI breaches as enterprises move from experimentation to production.
March 18, 2026 – Austin, TX – HiddenLayer, the leading AI security company protecting enterprises from adversarial machine learning and emerging AI-driven threats, today released its 2026 AI Threat Landscape Report, a comprehensive analysis of the most pressing risks facing organizations as AI systems evolve from assistive tools to autonomous agents capable of independent action.
Based on a survey of 250 IT and security leaders, the report reveals a growing tension at the heart of enterprise AI adoption: organizations are embedding AI deeper into critical operations while simultaneously expanding their exposure to entirely new attack surfaces.
While agentic AI remains in the early stages of enterprise deployment, the risks are already materializing. One in eight reported AI breaches is now linked to agentic systems, signaling that security frameworks and governance controls are struggling to keep pace with AI’s rapid evolution. As these systems gain the ability to browse the web, execute code, access tools, and carry out multi-step workflows, their autonomy introduces new vectors for exploitation and real-world system compromise.
“Agentic AI has evolved faster in the past 12 months than most enterprise security programs have in the past five years,” said Chris Sestito, CEO and Co-founder of HiddenLayer. “It’s also what makes them risky. The more authority you give these systems, the more reach they have, and the more damage they can cause if compromised. Security has to evolve without limiting the very autonomy that makes these systems valuable.”
Other findings in the report include:
AI Supply Chain Exposure Is Widening
- Malware hidden in public model and code repositories emerged as the most cited source of AI-related breaches (35%).
- Yet 93% of respondents continue to rely on open repositories for innovation, revealing a trade-off between speed and security.
Visibility and Transparency Gaps Persist
- Over a third (31%) of organizations do not know whether they experienced an AI security breach in the past 12 months.
- Although 85% support mandatory breach disclosure, more than half (53%) admit they have withheld breach reporting due to fear of backlash, underscoring a widening hypocrisy between transparency advocacy and real-world behavior.
Shadow AI Is Accelerating Across Enterprises
- Over 3 in 4 (76%) of organizations now cite shadow AI as a definite or probable problem, up from 61% in 2025, a 15-point year-over-year increase and one of the largest shifts in the dataset.
- Yet only one-third (34%) of organizations partner externally for AI threat detection, indicating that awareness is accelerating faster than governance and detection mechanisms.
Ownership and Investment Remain Misaligned
- While many organizations recognize AI security risks, internal responsibility remains unclear with 73% reporting internal conflict over ownership of AI security controls.
- Additionally, while 91% of organizations added AI security budgets for 2025, more than 40% allocated less than 10% of their budget on AI security.
“One of the clearest signals in this year’s research is how fast AI has evolved from simple chat interfaces to fully agentic systems capable of autonomous action,” said Marta Janus, Principal Security Researcher at HiddenLayer. “As soon as agents can browse the web, execute code, and trigger real-world workflows, prompt injection is no longer just a model flaw. It becomes an operational security risk with direct paths to system compromise. The rise of agentic AI fundamentally changes the threat model, and most enterprise controls were not designed for software that can think, decide, and act on its own.”
What’s New in AI: Key Trends Shaping the 2026 Threat Landscape
Over the past year, three major shifts have expanded both the power, and the risk, of enterprise AI deployments:
- Agentic AI systems moved rapidly from experimentation to production in 2025. These agents can browse the web, execute code, access files, and interact with other agents—transforming prompt injection, supply chain attacks, and misconfigurations into pathways for real-world system compromise.
- Reasoning and self-improving models have become mainstream, enabling AI systems to autonomously plan, reflect, and make complex decisions. While this improves accuracy and utility, it also increases the potential blast radius of compromise, as a single manipulated model can influence downstream systems at scale.
- Smaller, highly specialized “edge” AI models are increasingly deployed on devices, vehicles, and critical infrastructure, shifting AI execution away from centralized cloud controls. This decentralization introduces new security blind spots, particularly in regulated and safety-critical environments.
The report finds that security controls, authentication, and monitoring have not kept pace with this growth, leaving many organizations exposed by default.
HiddenLayer’s AI Security Platform secures AI systems across the full AI lifecycle with four integrated modules: AI Discovery, which identifies and inventories AI assets across environments to give security teams complete visibility into their AI footprint; AI Supply Chain Security, which evaluates the security and integrity of models and AI artifacts before deployment; AI Attack Simulation, which continuously tests AI systems for vulnerabilities and unsafe behaviors using adversarial techniques; and AI Runtime Security, which monitors models in production to detect and stop attacks in real time.
Access the full report here.
About HiddenLayer
ive AI applications across the entire AI lifecycle, from discovery and AI supply chain security to attack simulation and runtime protection. Backed by patented technology and industry-leading adversarial AI research, our platform is purpose-built to defend AI systems against evolving threats. HiddenLayer protects intellectual property, helps ensure regulatory compliance, and enables organizations to safely adopt and scale AI with confidence.
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hiddenlayer@sutherlandgold.com
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HiddenLayer Partners with Databricks
HiddenLayer is excited and proud to announce its strategic partnership with Databricks. HiddenLayer can now integrate with Databricks to increase the security of intellectual property through detecting and preventing adversarial machine learning attacks and scanning models for malicious code and vulnerabilities.
Introduction
HiddenLayer is excited and proud to announce its strategic partnership with Databricks. HiddenLayer can now integrate with Databricks to increase the security of intellectual property through detecting and preventing adversarial machine learning attacks and scanning models for malicious code and vulnerabilities.
There is little doubt that Artificial Intelligence is here to stay, with AI making headlines all over the news and becoming a hot topic of discussion across the globe. According to Gartner, "AI will be a critical driver of the next wave of digital innovation, creating $3.9 trillion in business value and 6.2 billion hours of worker productivity globally by 2022." Databricks is helping facilitate this meteoric rise of AI adoption as the creator of the lakehouse category and leader in the Machine Learning Operations (MLOps) market, while HiddenLayer is a pioneer in the research and defense of artificial intelligence application security.
Databricks Machine Learning, built on an open lakehouse architecture, is proven to empower ML teams to accelerate end-to-end ML. This new ability to integrate means the entire Databricks enabled MLOps lifecycle is now able to be secured right from your Databricks infrastructure - ensuring the most seamless, scalable and efficient Model security solution available on the market.
“Databricks + HiddenLayer is a powerful combination. Databricks has become an industry leader in ML Operations with MLflow and their model serving capability, helping data science teams design, develop, and deploy ML Models at a rapid pace. With HiddenLayer, companies can embed security throughout the entire ML Ops lifecycle from the cradle to the grave.” Howard Levenson, AI Industry Advisor.
Databricks & MLOps
Databricks and its Lakehouse Platform are used by data science teams worldwide for the following reasons:
- Collaboration: Databricks has a strong focus on collaboration and sharing, allowing multiple users to easily work on the same data and projects.
- Notebook environment: Databricks provides a notebook environment, similar to Jupyter Notebook, which allows data scientists to easily document their work, share their findings, and collaborate with others.
- Multi-language support: Databricks supports a wide range of programming languages, including Python, R, SQL, and Scala allowing data scientists to use their preferred language for data analysis and Machine Learning.
- Built-in libraries: Databricks provides built-in libraries for Machine Learning, such as TensorFlow, Keras, PyTorch, and scikit-learn, which makes it easy to perform advanced Machine Learning tasks.
- Data Management: Databricks Lakehouse platform provides a unified data management layer that allows users to easily access and analyze data from various sources, including structured and unstructured data, real-time streams, and data lakes. It also provides data catalog, data governance and data lineage features that allows for easy discovery, understanding and trust of the data.
- Advanced analytics: Databricks allows for easy integration with other open-source tools and libraries like DeltaLake, MLflow, and Koalas, which can help data science teams to perform advanced analytics such as time-series analysis, image recognition and natural language processing.
Security for Artificial Intelligence
With HiddenLayer’s partnership, Databricks can now add security and enhanced integrity to its long list of benefits provided to data science teams. Enterprise companies worldwide are rapidly incorporating artificial intelligence into their tech stack and introducing ML Models as a new cybersecurity attack surface which need to be monitored and protected.
Cyber Threat Actors are continuously evolving and devising new adversarial machine learning tactics and techniques. Given that many Machine Learning model inputs and predictions are publicly exposed, they are inherently vulnerable to these new attacks. According to Gartner, “Through 2022, 30% of all AI cyberattacks will leverage training-data poisoning, AI model theft, or adversarial samples to attack AI-powered systems.”
HiddenLayer’s MLSecPlatform and its flagship product HiddenLayer MLDR will protect your ML Models via the Databricks integration. HiddenLayer MLDR is a first of its kind cybersecurity solution that monitors, detects, and responds to Adversarial Machine Learning attacks targeted at ML Models. Our patent-pending technology provides a noninvasive, software-based platform that monitors the inputs and outputs of your Machine Learning algorithms for anomalous activity consistent with adversarial ML attack techniques. Response actions are immediate with a flexible response framework to protect your ML. Using HiddenLayer empowers your company to:
- Protect your intellectual property: Proprietary Machine learning models are the definition of critical intellectual property. If ML models are not secured, they may be used by unauthorized parties without permission, cloned, or stolen. Companies who proactively secure their ML models can safeguard their organization's intellectual property from being compromised.
- Ensure data privacy: Machine Learning models are often trained on large amounts of data, which can include sensitive information. Left unsecured, this data may be accessed by unauthorized parties, leading to potential data breaches and regulatory violations.
- Maintain accuracy: Machine Learning models can be reverse engineered, poisoned, and altered, leading to decreased accuracy, efficacy, and trustworthiness.
- Preserve your competitive advantage: Machine Learning models give companies advantages over the competition. Left unsecured, others may be able to replicate your results and catch up to you. Securing your models helps ensure that you maintain your competitive advantage.
How HiddenLayer Integrates with Databricks
The HiddenLayer-Databricks integration wraps an ML model as it is registered (saved) in Databricks Lakehouse. The integration is model agnostic and includes model scanning and model detection and response. This enables Data Scientists and ML Engineers to add security to their models with no code or behavioral changes to their environment. As the model is loaded, it will be scanned by HiddenLayer's model scanner to ensure integrity as well as security. If an attack is detected, the integration will handle the response accordingly without any human interaction needed. With the peace of mind of ML Models protected by HiddenLayer, Data Science teams can focus their attention on building their advantage without sacrificing integrity or security.
Conclusion
Incorporating security into machine learning operations is critical for data science teams. With the increasing use of machine learning models in sensitive areas such as healthcare, finance, and national security, it is essential to ensure that machine learning models are secure and protected against malicious attacks. By embedding security throughout the entire machine learning lifecycle, from data collection to deployment, companies can ensure that their models are reliable and trustworthy.
Databricks Lakehouse Platform enables data science teams to design, develop, and deploy their ML Models rapidly while HiddenLayer MLSec Platform provides comprehensive security to protect, preserve, detect, and respond to Adversarial Machine Learning attacks on those models. Together, the two solutions empower your company to rapidly and securely deliver on your mission to advance your Artificial Intelligence strategy.
To learn more or try HiddenLayer’s integration with Databricks, please contact info@hiddenlayer.com.
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