Explore
.png){kind=logo}
Explore
.png)
Despite relying on “secure” AI platforms from the leading hyperscalers and SaaS providers, 73% of enterprises have reported AI-related security incidents. That statistic reveals a harsh reality: platform-native security isn’t enough.
As AI agents move from pilots to production—reasoning, acting, and collaborating autonomously—enterprises face a new threat model. Platform providers offer useful security baselines, but they weren’t built to handle the speed, complexity, and context-specific risks of agentic AI in the enterprise.
While cloud and SaaS providers offer important security baselines, they are not designed to meet enterprise-specific needs. Some of the key gaps include:
Cloud platforms are built for scale and simplicity. Their security controls are designed to serve a wide range of customers, not the unique compliance, operational, or risk demands of a specific enterprise.
A healthcare AI assistant processing patient records must meet HIPAA auditability standards—something a generic content filter can’t enforce. A trading agent analyzing markets must detect subtle prompt injections, not just block explicit language. Platform controls are too blunt for high-stakes, domain-specific scenarios.
No enterprise uses a single AI platform. Most environments are multi-modal and multi-cloud, utilizing APIs from providers like Azure OpenAI for customer engagement, Claude on AWS Bedrock for code generation, Google Gemini for image generation, and more. Each platform has its own security posture and native controls, leaving enterprises to manage a patchwork of disparate tools. The challenge is the inconsistency this creates. Security policies become fragmented, enforcement varies by provider, and blind spots emerge. Attackers exploit these gaps, using tactics like cross-platform prompt injections and agent-to-agent manipulations to slip past protections. Platform-native controls built for native models simply lack the visibility or authority to defend across boundaries.. AI models are evolving at unprecedented speed, and organizations are adopting a multi-provider strategy to stay ahead.
Cloud providers, AI model vendors, and even open source communities all prioritize rapid innovation and usage growth. Security tends to lag. Security, by contrast, is almost always a trailing concern—treated as a checkbox rather than a core design principle, and rarely equipped with the sophistication required for production-grade enterprise deployments. New features like tool calling, multi-agent frameworks, or context-sharing APIs often launch without mature risk controls, leaving enterprises to discover and address the consequences post-deployment. The result is that organizations are left to uncover risks after adoption, when fixes are costly and disruptive.
Meanwhile, AI models are evolving faster than ever. Enterprises cannot afford to lock themselves into a single provider; they need the freedom to adopt the models that best meet their needs at any given time. That makes security even more complex: protection must be holistic, cloud, provider, and model agnostic, while still delivering rigorous controls, auditability, and explainability.
A unified security layer solves this challenge, giving enterprises both the flexibility to innovate and the confidence to deploy AI safely at scale. Security isn’t just about what platforms protect—it’s about what they miss.
Eighty-nine percent of AI agent attacks occur at runtime, when agents are reasoning, calling tools, and making decisions. Most platform-native tools focus on validating pre-deployment configurations or reviewing logs, but they often miss active threats. This oversight can lead to actions such as prompt injection, altering decision logic, an agent with elevated privileges accessing unauthorized systems, or a rogue tool surfacing with malicious intent mid-conversation.
These live, context-driven threats require modern tools built to analyze agent behavior in real time and enforce policy mid-interaction.
Regulatory requirements like the EU AI Act, HIPAA, SOX, or FINRA must be explainable, auditable, and aligned with company policies. They require detailed audit trails, explainable decisioning, and real-time risk detection that platform-native controls can’t deliver alone.
For example, a bank must justify an AI agent’s investment decision to regulators. Platform logs show input/output, but not how the agent synthesized data, evaluated risk, or arrived at a recommendation. Without deeper context, observability breaks down, and governance fails.
Enterprise security must go beyond binary “block” or “allow” responses. It needs to understand and supervise why an AI agent made a decision and how that aligns with corporate policy, ethical standards, and regulatory mandates.
Platform security APIs don’t expose the internal logic, decision paths, or tool interactions of complex AI agents. That’s where third-party observability, interpretability, and control layers become essential.
Platform providers prioritize rapid innovation and usage growth—security tends to lag. New capabilities like tool calling, multi-agent frameworks, and context-sharing APIs often launch without mature, real-time risk controls, leaving enterprises to discover and address the consequences post-deployment. But even if platforms could keep pace with their own innovations, they’re not built to secure the complex, layered architectures enterprises actually deploy. In practice, organizations combine models from multiple providers, integrate internal tools and APIs, and build custom AI applications tailored to specific workflows. These environments demand context-aware protection that platform-native security simply wasn’t designed to deliver. When agents span clouds, invoke proprietary tools, and act autonomously, security needs to be as dynamic and distributed as the architecture itself.
Security isn’t just about what platforms protect—it’s also about what they miss.
The average cost of an AI-related breach now exceeds $4.8M, but that figure often understates the full damage:
Meanwhile, enterprise-grade AI agent security costs a fraction of that for large-scale deployments. The ROI is immediate: reduced risk, faster deployments, and stronger compliance.
AI agents operate at machine speed. Platform tools add hundreds of milliseconds of latency and still miss subtle threats. Modern third-party security solutions operate with low latency across high-throughput environments, delivering deep protection without disrupting performance.
For industries like trading, logistics, or emergency medicine, that difference is existential. Relying solely on native security often overlooks critical issues, including prompt injections that bypass filters during inter-agent handoffs, shadow tools embedded in workflows that go undetected, and platform APIs that fail to explain how AI recommendations are generated. By implementing security that operates across platforms, understands agent behavior, and responds in real time, organizations are able to achieve faster innovation, safer operations, and stronger compliance.
Specialized security platforms complement cloud provider offerings by delivering:
Organizations should prioritize enhanced AI agent security when:
experiencing (or anticipating) runtime attacks or governance gaps
Cloud and SaaS platforms provide essential foundations, but as enterprises shift to production AI, platform-native security alone is not enough. Enterprises need controls that are layered, specialized, and adapt as quickly as their agents do, so they can adopt AI at scale without compromising safety, speed, or compliance.
Don’t wait for a breach to get serious about AI agent security. Enterprises that proactively take a layered, specialized approach to AI security today are safer, faster, more scalable, and will have a lasting competitive advantage.
.png){kind=logo}
.png)
.png)