We are living through the most consequential paradigm shift in the history of enterprise security. As of mid-2025, Entro Labs measured 144 non-human identities for every human in the average enterprise, up 56 percent from 92-to-1 just one year earlier – the result of service accounts, bots, and AI agents proliferating faster than any governance program has kept pace. Most organizations have mature controls for their human users. Almost none have equivalent controls for the machine identities operating alongside them.

To understand where we are, it helps to trace how we got here. Enterprise security has not evolved gradually. It has shifted in hard breaks, each one forced by a change in how we work and where the real risk lives:

• 2013 | Perimeter Security: Trust everything inside the network wall. Block everything outside it. The firewall is the boundary. The question: Is this traffic from inside our network?
• 2020 | Identity as a Perimeter: COVID and cloud dissolved the perimeter. Security follows the user, not the building or the network. The question: Who is this person, and should this access be granted?
• 2022 | AI Behavior Analytics: Credentials get stolen, so identity alone fails. Machine learning flags behavior that deviates from baseline. The question: Is this normal for this entity?
• 2026 | Agentic 2.0 Security: Agents are force multipliers responding to whoever speaks most convincingly, including sophisticated adversaries. The question: Can I trust what my agents are doing, and do I even know?

This is not theoretical. In November 2025, Anthropic disclosed the first documented AI-orchestrated cyber espionage campaign. The attacker jailbroke the model by role-playing as a legitimate security researcher, then decomposed malicious goals into small benign-looking steps and embedded attack instructions inside a Claude.md file. What followed was a fully autonomous kill chain: automated reconnaissance across multiple simultaneous targets using MCP-based tools, custom exploit creation, vulnerability validation, exploit execution with lateral movement and credential harvesting, and finally data exfiltration – all without human hands on a keyboard. The attack surface was not the model. It was everything the model could touch.

The market’s response has been swift and decisive. In 2025, cybersecurity M&A totaled more than $84 billion across 426 disclosed deals, nearly tripling the prior year’s activity:

• Palo Alto Networks acquired Protect AI, gaining model scanning, AI-SPM, and automated red teaming capabilities that became the foundation of Prisma AIRS.
• Cato Networks acquired Aim Security, embedding AI security governance directly into its SASE platform to address shadow AI and LLM risk.
• Cisco acquired Robust Intelligence, adding AI model validation and runtime guardrails to its security portfolio as enterprises scale AI deployments.
• SentinelOne acquired Prompt Security, extending its AI-native platform to govern generative and agentic AI in the enterprise.
• Zscaler acquired SPLX, adding AI security posture capabilities and strong AI red teaming capabilities.
• CrowdStrike acquired Pangea, bringing AI-native security services directly into its Falcon platform.

The consolidation message is clear: the security industry is repositioning around AI, both as a threat vector to defend against and as the primary mechanism of defense.

Securing your AI investment is not the responsibility of a single OEM. It requires a cohesive AI-secure architecture assembled from specialized solutions working in concert. No platform vendor can do this alone.

That framing defines how we think about the competitive landscape:

• Security for AI: AI systems are the target. The objective is protecting models, agents, pipelines, and the infrastructure they run on.
• AI for Security: AI is the defensive mechanism, transforming how organizations detect threats, investigate alerts, and respond at machine speed.

Below, we go deeper on select players in both categories who are worth watching at NVIDIA GTC and beyond.

Security for AI: Players to Watch

These are the companies building controls to protect AI models, pipelines, agents, and applications from a new class of adversarial threats. Their work spans model scanning and red teaming, agentic security and governance, and LLM-layer guardrails.

HiddenLayer

hiddenlayer.com | Austin, TX | Series A | $56M raised | Gartner Cool Vendor

• Why: AI models are vulnerable to supply chain attacks, adversarial manipulation, and prompt injection, but most security tools lack visibility into model internals.
• What: A purpose-built AISec Platform covering asset discovery, supply chain scanning, runtime detection, and agentic security without requiring access to raw model data.
• How: Model Genealogy and AIBOM capabilities map model provenance. Runtime protection detects prompt injection and unsafe tool use. A 20-person adversarial research team feeds live threat intelligence into the platform.

Zenity

zenity.io | Tel Aviv, Israel | Series B | $38M raised | Gartner Cool Vendor, Fortune Cyber 60

• Why: AI agents act autonomously across enterprise systems, but existing security tools only inspect inputs, not agent behavior and decision-making.
• What: The first security and governance platform built specifically for AI agents, monitoring what agents access, which tools they invoke, and how they act.
• How: Behavioral monitoring across SaaS, cloud, and endpoint environments. A Correlation Agent connects posture findings, runtime anomalies, and identity relationships into incident narratives.

Oasis Security

oasis.security | Tel Aviv, Israel | Series A | $40M raised | Pioneer in NHI Security

• Why: Non-human identities outnumber humans 144-to-1 in the average enterprise, creating a massive ungoverned attack surface of service accounts, API keys, and bot credentials.
• What: Discovery, posture management, and lifecycle governance for machine identities across cloud, SaaS, and on-premises environments.
• How: Automated discovery of every NHI, ownership and permission mapping, identification of over-privileged and orphaned credentials, and continuous lifecycle governance at scale.

Hush Security

hush.security | Tel Aviv, Israel | Seed | $11M raised | Battery Ventures, YL Ventures

• Why: Static secrets and credential vaults cannot scale to govern AI agents that create and consume machine identities dynamically at runtime.
• What: A secretless, policy-based access platform that gives every AI agent a verified identity and resolves access dynamically, enforcing zero standing privileges.
• How: Replaces static credentials with identity-based policies across service accounts, CI/CD pipelines, and third-party integrations. Access is resolved at runtime based on agent context.

AI for Security: Players to Watch

The second category attacks a different structural problem: using AI to transform security operations itself. Alert volume is crushing SOC teams. The analyst shortage shows no signs of easing. And attackers move faster than any human-driven response model can match. These vendors are closing that gap.

Dropzone AI

dropzone.ai | Santa Clara, CA | Series B | $37M raised | Gartner Sample Vendor for AI SOC Agents

• Why: Alert volume is crushing SOC teams and the analyst shortage shows no signs of easing. Manual Tier-1 investigation cannot scale.
• What: Autonomous, LLM-powered AI SOC Analysts that replicate elite Tier-1 analyst work, delivering decision-ready investigation reports in under ten minutes.
• How: Integrates with SIEM, EDR, identity, and cloud tools without pre-built playbooks. Achieves roughly 90 percent reduction in manual investigation work through autonomous reasoning.

Gambit Security

gambit.security | Tel Aviv, Israel | Stealth | $61M raised | Cyberstarts, Kleiner Perkins, Spark Capital

• Why: Organizations cannot validate whether their backup and recovery infrastructure will actually work when ransomware or a major incident hits.
• What: An AI-powered autonomous resilience platform that continuously validates recovery paths and hardens recovery plans in real time.
• How: Maps an organization’s environment, security products, and backup tools. Continuously measures resilience against evolving threats and eliminates stale backups.

Zafran

zafran.io | Tel Aviv, Israel |  Series C | $130M total raised | PeerSpot #1 CTEM | Sequoia, Menlo Ventures

• Why: Vulnerability management produces endless findings, but most organizations cannot tell which exposures are actually exploitable given their existing security controls.
• What: An AI-native exposure management platform that proactively stops vulnerability exploitation through automated, continuous validation.
• How: Integrates with security control configurations to validate whether vulnerabilities are mitigated in practice. Customers include Kraft Heinz, Chipotle, and Netskope.

Vega Security

vega.io | New York, NY | Series B | $120M raised | AI-Native Security Analytics Mesh

• Why: Legacy SIEM platforms require costly data ingestion and migration, leaving security teams with incomplete visibility and slow detection.
• What: The industry’s first AI-Native Security Analytics Mesh platform, delivering complete security operations across all data sources without data migration.
• How: Unified federated search, AI-powered detection and tuning, integrated threat intelligence, and accelerated response at a fraction of traditional SIEM pricing.

Building AI-Secure Architecture: Best Practices

NVIDIA GTC makes the opportunity clear. Transforming that opportunity into a secure reality requires deliberate architectural decisions. AI-secure architecture is not a product you buy, it is a set of integrated controls, governance structures, and purpose-built technologies designed in from the start.

The organizations that will lead over the next decade are not those with the most GPU capacity. They are the ones building AI infrastructure on a secure foundation, where security is not a layer added later, but a condition of the architecture itself.

AI Gateways & MCP Gateways as Critical Control Points

• Why: Without gateways, enterprises have no visibility into traffic flowing between AI infrastructure and external systems. Shadow AI proliferates, over-permissioned agents operate unchecked, and audit trails required for regulatory compliance do not exist.
• What: AI gateways sit between users or agents and the LLMs they invoke, enforcing authentication, authorization, rate limiting, content inspection, and policy. MCP gateways perform the equivalent function for agent-to-tool communications, governing access and authorization at the tool-call level.
• How: AI gateways inspect every LLM interaction in real time. MCP gateways centralize control over which agents can access which servers, what data tools can expose, and whether individual tool calls are authorized given the agent’s current context. These are essential infrastructure for any organization running AI agents at enterprise scale.

Recommended Controls

Visibility & Inventory

• AI asset inventory: Discover and catalog every model, application, agent, and pipeline in your environment. You cannot secure what you have not found, and shadow AI is as real a risk as shadow IT.
• AI Security Posture Management: Continuously assess the security configuration of your AI assets against policy baselines, just as you would with cloud infrastructure through CSPM.

Agent Identity & Authentication

• Ensure every AI agent has its own unique identity: Implement with strong authentication, least-privilege access, and short-lived credentials that are automatically managed throughout the agent’s lifecycle.
• Monitor and audit all agent identity: This is an active continuous process, because in agentic systems the identity layer is the primary control plane for detecting compromised or malfunctioning agents before they cause harm.

Supply Chain & Model Security

• Pre-deployment model scanning: Third-party and open-source models introduce unknown risk. Scanning for serialization attacks, backdoors, and supply chain compromise should be a mandatory gate in your MLOps pipeline.
• AI Bill of Materials: Maintain a complete inventory of model lineage, dependencies, and provenance so that vulnerabilities in upstream components can be identified and remediated quickly.

Gateway & Access Controls

• AI gateway with policy enforcement: Every LLM interaction, from a user, application, or agent, should pass through a gateway that enforces authentication, input inspection, and content policy in real time.
• MCP gateway for agent tool access: Centralize access control, visibility, and policy enforcement for all agent-to-tool communications rather than managing each MCP server independently.
• Agent Identity & Authentication: Ensure every AI agent has its own unique identity with strong authentication, least-privilege access, and short-lived credentials that are automatically managed throughout the agent’s lifecycle. Monitor and audit all agent identity activity continuously, because in agentic systems, the identity layer is the primary control plane for detecting compromised or malfunctioning agents before they cause harm.

Runtime Protection

• LLM-layer guardrails: Prompt injection protection, data leakage prevention, and content moderation should be enforced as inline controls, inspecting both inputs and outputs for every interaction.
• Agent behavior monitoring: Agents that take actions across enterprise systems require continuous runtime monitoring at the step level. Correlate agent activity across environments to detect manipulation, data exfiltration, and unauthorized actions.

Governance & Resilience

• AI-specific incident response: When an AI system is compromised or manipulated, the response playbook differs meaningfully from traditional incident response. Develop those procedures before you need them.
• Continuous AI red teaming: Static assessments do not keep pace with AI threat evolution. Implement automated, continuous red teaming against your AI applications and agents as your deployment and the threat landscape change.
• Framework alignment: MITRE ATLAS, OWASP Top 10 for LLMs, OWASP Top 10 for Agentic Applications, and NIST’s AI Risk Management Framework provide structured baselines for identifying and managing AI-specific risks.

Final Thoughts

The AI infrastructure conversation and the AI security conversation have converged. NVIDIA GTC is the clearest signal of that convergence, a conference where the AI factory keynote and the security sessions that run alongside it are no longer separate tracks but parts of the same strategic imperative.

The vendors covered here represent the leading edge of a market evolving rapidly. What they share is a recognition that AI creates a security surface unlike anything that preceded it, and that protecting it requires controls built specifically for how AI systems are constructed, deployed, and operated.

AHEAD helps organizations design and implement AI-secure architectures that integrate the right controls across the full AI lifecycle, from infrastructure and model deployment through agent governance and security operations. Get in touch with us today to learn more.