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Protecting Machine Learning Models, Large Language Models, and Enterprise AI Workloads from Adversarial Threats
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Identify which approach suits your organisation.
1. What is your primary need?
Comprehensive coverage → Protect AI | Specialised capability → CalypsoAI
2. What is your scale?
Enterprise (1,000+ employees) → Platform approach | Mid-market → Focused solution
3. What is your maturity?
Established security programme → Advanced capabilities | Building out → Comprehensive platform
The vast majority of enterprises have deployed AI without implementing AI-specific security controls. Traditional cybersecurity tools cannot detect adversarial attacks against AI systems.
Adversarial attacks against AI systems have increased 3,000% since 2023 as attackers target high-value AI models that process sensitive data and make business-critical decisions.
The EU AI Act mandates AI system security including adversarial robustness, data integrity, and ongoing monitoring. Compliance requires AI security capabilities that most organisations have not yet implemented.
AI security is projected to become one of the largest cybersecurity categories. Organisations that establish AI security programmes now build the foundation for compliance and protection as AI deployment scales.
In-depth analysis for buyers and investors evaluating ai security companies 2026.
The enterprise adoption of artificial intelligence has outpaced the development of security controls to protect it. While 77% of enterprises have deployed AI in some form, the vast majority lack AI-specific security measures. Traditional cybersecurity tools — firewalls, endpoint protection, SIEM — were designed to protect networks, devices, and applications. They cannot detect adversarial attacks against machine learning models, prompt injection targeting LLMs, or data poisoning corrupting training datasets because these attacks operate within the AI inference pipeline rather than through conventional attack vectors.
This security gap is creating the fastest-growing category in cybersecurity, projected to reach $60B+ by 2030. The growth is driven by two converging forces: rapidly increasing enterprise AI deployment creating the demand, and rapidly increasing adversarial AI attacks creating the urgency. Adversarial attacks against AI systems have increased 3,000% since 2023 as attackers recognise that AI models represent high-value targets — they process sensitive data, make business-critical decisions, and often lack the security monitoring that traditional IT systems receive.
AI systems face five primary attack categories. Adversarial attacks manipulate model inputs to produce incorrect outputs — subtly altered images that fool computer vision, or carefully crafted text that bypasses content filters. Data poisoning corrupts training data to create hidden backdoors or biases in the model. Model extraction enables attackers to steal proprietary models by systematically querying them and reconstructing the architecture. Prompt injection forces LLMs to ignore instructions, reveal system prompts, or execute unintended actions.
The fifth category — AI supply chain attacks — targets the dependencies that AI systems rely on. Pre-trained models downloaded from public repositories, open-source ML libraries, and training datasets sourced externally can all contain vulnerabilities or malicious code. Protect AI's research has identified thousands of malicious models on public repositories like Hugging Face. For enterprises deploying AI, securing the AI supply chain requires the same rigour applied to software supply chain security — model scanning, dependency verification, and provenance tracking.
Buyer's Note: When evaluating ai security companies 2026, request demonstrated results from environments similar to yours. Vendor claims about detection rates and coverage should be validated against your specific technology stack and threat landscape.
Large language models present unique security challenges that differentiate them from traditional ML models. LLMs process natural language, making them uniquely susceptible to prompt injection — attacks where malicious instructions are embedded in user inputs, system contexts, or retrieved documents that override the model's intended behaviour. A successful prompt injection can cause an LLM to reveal confidential information from its system prompt, generate harmful content that bypasses safety controls, or execute unauthorised actions in agentic AI systems.
Securing LLMs requires real-time inspection of both inputs (user prompts, retrieved context) and outputs (model responses) to detect and prevent attacks before they succeed. CalypsoAI and similar platforms act as security proxies between users and LLM providers, analysing every interaction for indicators of prompt injection, data exfiltration, policy violations, and adversarial manipulation. For enterprises deploying customer-facing LLM applications, this inspection layer is not optional — a single successful prompt injection that exposes customer data or generates harmful content creates regulatory, legal, and reputational risk.
The EU AI Act, effective in 2026, introduces the first comprehensive regulatory framework for AI systems. High-risk AI applications require conformity assessments, technical documentation, human oversight mechanisms, and ongoing monitoring. The AI Act creates specific requirements for AI security — ensuring AI systems are robust against adversarial attacks, protecting the integrity of training data, and maintaining audit trails of AI system behaviour. Organisations deploying AI in the EU must demonstrate compliance with these requirements.
AI security platforms are evolving to address governance requirements alongside threat protection. Features including AI model inventories (knowing what AI is deployed across the organisation), AI Bills of Materials (documenting model components and dependencies), and continuous compliance monitoring (validating AI systems meet regulatory requirements) transform AI security from a pure technical capability into a governance function. For organisations subject to the EU AI Act, AI security platforms that provide both protection and compliance evidence satisfy dual requirements through a single investment.
GenAI Warning: Generative AI is reshaping cybersecurity — both as a defence multiplier and a threat amplifier. Evaluate how each vendor incorporates AI into their capabilities and how they address AI-specific threats including adversarial AI, deepfakes, and automated attack generation.
Organisations beginning their AI security journey should start with three foundational capabilities. First, AI asset inventory — understanding what AI models are deployed, where they operate, what data they access, and who manages them. Most organisations discover they have significantly more AI deployments than leadership realises, including shadow AI usage by individual teams. Second, LLM security controls for any customer-facing or business-critical LLM applications, implementing input/output inspection to prevent prompt injection and data leakage. Third, AI supply chain security for any AI development activities, scanning models and dependencies before they enter production.
The maturity path progresses from reactive controls (scanning and monitoring) to proactive governance (AI policies, automated compliance, red team testing). Advanced AI security programmes include adversarial testing of production AI systems, continuous model monitoring for drift and degradation, and integrated AI risk management that connects AI security findings with enterprise risk frameworks. Given the nascency of the category, even foundational capabilities provide significant risk reduction compared to the 77% of organisations that currently deploy AI with no AI-specific security controls.
The AI security vendor landscape is nascent but developing rapidly. Dedicated AI security startups — Protect AI, HiddenLayer, CalypsoAI, Robust Intelligence, Lasso Security — address AI-specific threats that traditional vendors cannot. Their advantage is focus: purpose-built technology for AI threats rather than retrofitted capabilities from traditional security products. Their limitation is scale: most are early-stage companies with limited deployment history in large enterprise environments.
Platform cybersecurity vendors are beginning to add AI security capabilities, though depth varies significantly. Palo Alto Networks and CrowdStrike have announced AI security features, primarily through AI-powered threat detection rather than protection of AI systems themselves. Microsoft's AI security integrates with Azure AI services but is limited to the Microsoft ecosystem. For the near term, enterprises deploying significant AI will likely need dedicated AI security vendors alongside their existing platform vendors, as platform AI security capabilities are not yet mature enough to provide comprehensive protection.
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