Enterprises Are Adopting AI Fast—But Leaving Security Behind, Says SandboxAQ Report
In its first AI Security Benchmark Report, released today, SandboxAQ exposed a widening gap between how enterprises are deploying AI and how prepared they are to secure it. Based on a survey of over 100 senior security leaders across the U.S. and EU, the report reveals that 79% of organizations are already running AI in production, yet only 6% have implemented AI-native security protections across both IT and AI infrastructure.
It’s a wake-up call for security and IT leaders in highly regulated sectors like finance, healthcare, and telecom: while AI adoption is racing ahead, security strategy is lagging dangerously behind.
“This isn’t just a solution gap—it’s a conceptual one,” said Marc Manzano, GM of Cybersecurity at SandboxAQ. “AI is changing the cybersecurity paradigm at machine speed. Most security teams are using outdated playbooks for a radically new threat model.”
Findings from the Report:
- AI-Native Security Lacking: Only 6% of organizations have deployed AI-specific protections across IT and AI systems.
- CISO Concerns Mounting: 74% of respondents are highly concerned about AI-enabled cyberattacks; 69% worry AI will expose new vulnerabilities.
- Responsibility Gaps: Just 10% of companies have a dedicated AI security team—most are leaning on traditional IT and InfoSec teams.
- AI Systems Are Operating Autonomously: Non-Human Identities (NHIs)—autonomous AI agents and services—pose major risks due to lack of visibility, governance, and cryptographic hygiene.
The Rise of NHIs: A New Blind Spot in Identity Security
The report spotlights a fast-emerging challenge: Non-Human Identities (NHIs)—AI agents, services, and machine accounts—are proliferating across enterprise infrastructure, often interacting with systems, holding credentials, and accessing sensitive resources with little or no human oversight.
These NHIs often fall outside existing identity governance frameworks, breaking Zero Trust assumptions and increasing the risk of credential misuse, data leakage, and lateral movement attacks.
Manzano warned that current identity security models, built around human users, are simply not equipped to manage machine-speed, autonomous systems.
Legacy Tools Can’t Handle AI Threats
A key insight from the report: traditional security tools—rules-based, reactive, and manual—are misaligned with AI’s dynamic and fast-evolving attack surface. The modern AI stack includes:
- Model training pipelines
- AI-generated outputs
- Autonomous inference engines
- API-driven agents
Yet most organizations are still relying on firewalls, SIEMs, and static IAM policies that offer little protection against adversarial machine learning, model theft, or AI-led reconnaissance.
AQtive Guard: SandboxAQ’s Response
In response, SandboxAQ offers AQtive Guard, its AI-native security solution that modernizes cryptographic and identity governance for the AI era. The platform automates inventory, visibility, and policy enforcement for both traditional IT and next-gen AI assets—an essential shift for organizations managing large-scale, distributed AI deployments.
The Future: More Spend, But Still a Long Road Ahead
Despite the security lag, investment is growing fast. The report finds:
- 85% of organizations plan to increase AI security spending in the next 12–24 months
- A quarter of enterprises expect to make significant increases
- Key investment areas include:
- Protecting training data and inference pipelines
- Securing NHIs
- Deploying AI-aware incident response
Yet SandboxAQ cautions that throwing money at the problem without rethinking architecture will not be enough.
“You can’t patch your way into AI security,” Manzano said. “You need to re-architect for a world where machines—not just humans—are your users, attackers, and defenders.”
The AI Era Needs AI-Native Security
The 2025 AI Security Benchmark Report underscores a critical truth: AI has already changed how enterprises operate—now it’s changing how they’re attacked. To defend effectively, organizations must adopt AI-native security models that account for NHIs, machine-speed threats, and evolving adversarial techniques.
The full report is available now from SandboxAQ.
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