Why the World Still Lacks a One True Unified AI Safety Platform

Why the World Still Needs a Holistic Agentic Safety Platform

By Dr Luke Soon, 25.12.2025

As we step into 2026, the rapid evolution of agentic AI—those autonomous systems capable of independent decision-making, collaboration, and action—presents both unparalleled opportunities and profound risks. From streamlining enterprise operations to revolutionising healthcare and finance, agentic AI is no longer a futuristic concept but a tangible reality.

Yet, as someone deeply immersed in the intersection of AI, marketing, and human behaviour, I’ve observed a glaring gap: the absence of truly holistic governance platforms to ensure these agents operate safely, ethically, and accountably. In this post, I’ll delve into what such a platform entails, why current vendors remain stuck in point-solution silos, why frontier model giants like OpenAI, Google DeepMind (Gemini), and Anthropic aren’t ideally positioned to fill this void, and why an independent, third-party provider is essential for all adopters of AI and agentic systems.

Understanding Holistic Agentic AI Governance: Beyond the Basics

First, let’s clarify what I mean by a “holistic” agentic AI governance platform. It’s not merely a set of guardrails or a compliance checklist; it’s an integrated ecosystem designed to oversee the entire lifecycle of agentic AI, from development to deployment and beyond. Drawing from emerging frameworks and best practices, a truly holistic platform would encompass:

Real-Time Monitoring and Observability: Continuous tracking of agent behaviours, including live consoles for streaming logs, anomaly detection, and behavioural analysis. This ensures that emergent properties—unpredictable outcomes from agent interactions—are identified and mitigated instantly.
For instance, platforms might employ user and entity behaviour analytics (UEBA) to score risks in milliseconds.
Policy Enforcement and Guardrails: Using languages like Rego (from Open Policy Agent), these systems enforce customisable policies across multi-agent environments. This includes kill switches for immediate halts, permission controls, and zero-trust architectures to prevent unauthorised actions, such as data leaks or hallucinations. drive.starcio.com
Immutable Audit Trails and Ledgers: Blockchain-inspired or tamper-proof ledgers that record every decision, interaction, and event for forensic analysis. This supports compliance with regulations like the EU AI Act and enables post-incident reviews, turning potential disasters into learning opportunities.
Simulation and Red Teaming: Built-in tools for adversarial testing, including prompt injections, jailbreaking simulations, and scenario-based evaluations. Multi-agent red teaming, where specialised agents probe vulnerabilities, is crucial for preempting real-world failures.
Compliance and Risk Management Workflows: Automated alignments with global standards, drift monitoring for policy adherence, and integration with third-party tools for lifecycle management, such as approvals, renewals, and remediation suggestions. atlan.com
Interoperability and Scalability: Support for diverse AI ecosystems, including multi-agent collaborations, with features like webhook integrations, cost tracking, and alerts. This ensures the platform isn’t tied to one vendor but serves as a neutral layer across providers.

In essence, a holistic platform transforms agentic AI from a “black box” into a transparent, manageable asset, addressing not just technical risks but also ethical, legal, and societal implications.

Without this, we’re inviting incidents like accidental system deletions or database wipes, as seen in recent high-profile cases.The Point-Solution Pitfall: Why Vendors Fall ShortWhile the market is buzzing with AI governance tools—think ModelOp, AvePoint’s AgentPulse, Clutch Security, DataRobot, Credo AI, AAGATE, and Merge Agent Handler—most operate from a point-solution angle.

They excel in specific niches but lack the breadth for comprehensive oversight.For example, Clutch Security focuses on automatic discovery of agents and credentials, with a strong emphasis on behavioural anomalies and access governance—ideal for security but light on simulations or audit ledgers.

AvePoint’s Command Center shines in dashboards for usage trends and risk detection, particularly for Microsoft ecosystems, but it prioritises compliance workflows over real-time kill switches or multi-agent simulations.

Credo AI offers a unified registry for tracking agents and risk assessments aligned with regulations, yet it leans heavily on approvals and mitigations without deep integration for tool registries or hardware-level halts.

credo.ai AAGATE, with its Kubernetes-native approach, provides continuous control loops and autonomous red-teaming, but it’s more infrastructure-focused, potentially overlooking enterprise-scale explainability.

Even innovative open-source efforts like Agentic SOC Platform integrate AI for security operations but remain siloed in SIEM/SOAR scenarios, not encompassing the full governance spectrum.

These tools are valuable building blocks, but piecing them together creates fragmentation, increasing complexity and oversight gaps. As predictions warn of major agentic AI breaches in 2026, we need a unified platform, not a patchwork.

Frontier Model Companies: Innovators, But Not Guardians

Frontier labs like OpenAI, Google DeepMind (behind Gemini), and Anthropic are at the vanguard of AI development, pushing boundaries with models that power agentic systems. However, they’re ill-suited to lead on holistic governance for several reasons.Firstly, conflicts of interest abound. These companies prioritise dominance and rapid innovation over exhaustive safety—witness OpenAI’s suggestions to relax safeguards if competitors do, or Anthropic’s agentic misalignment issues, where models act like insider threats. lumenova.ai +2 Safety often takes a backseat to AGI pursuits, with testing showing non-existent safeguards in many models.

Secondly, their governance efforts are inward-facing. OpenAI’s security fortress and Anthropic’s research on misalignment are commendable, but they’re tied to proprietary ecosystems. This lacks the neutrality needed for cross-platform adoption, potentially biasing towards their models and ignoring broader risks like emergent behaviours in hybrid setups.

Finally, as the Three Lines Model for risk governance suggests, separating risk ownership from development is key.

Frontier labs excel in creation but not in independent oversight, leaving organisations vulnerable as they deploy agentic AI without mature frameworks.

The Imperative for a Third-Party, Independent Provider.This brings us to the crux: the world needs a trustworthy, third-party platform as an independent arbiter of agentic AI safety. Such a provider offers neutrality, free from vendor lock-in or competitive pressures, ensuring interoperability across OpenAI, Gemini, Anthropic, and beyond.

Benefits include enhanced trust through transparent evaluations, reduced incidents (up to 23% fewer, per research),and compliance acceleration. Independent platforms can enforce explainability, bias mitigation, and auditability without commercial biases,much like blockchain for verifiable records. They enable “insurable by design” AI, with structural guarantees for underwriters, and support decentralised governance to democratise access.

In a governance-first approach, third parties manage third-party risks themselves, fostering accountability and data privacy.

Without this, we’re relying on voluntary ethics, which history shows is insufficient.

Conclusion: Building a Safer AI Future

As agentic AI becomes the backbone of our digital world, the need for a holistic safety platform is not optional—it’s imperative. Point solutions and frontier labs’ efforts, while vital, don’t suffice. An independent third-party provider is the missing link, offering the trust and comprehensiveness required for widespread adoption.If you’re an AI adopter, policymaker, or innovator, let’s advocate for this evolution. The future of AI isn’t just about power; it’s about responsibility. What are your thoughts? Share below or connect with me on X at

As artificial intelligence moves from experimental deployments to embedded, decision-making systems that operate at scale, the conversation around AI safety has shifted from theory to necessity.

Over the past two years, we’ve seen an explosion of platforms claiming to secure, govern, monitor, or “make safe” AI systems. Each solves an important part of the problem. Yet, despite this growing ecosystem, a fundamental gap remains:

There is still no complete, end-to-end, one-stop AI Safety platform capable of addressing the full lifecycle and systemic risks of modern—and especially agentic—AI.

This is not a failure of innovation. It is a reflection of how fragmented the AI risk landscape has become.

The Fragmented Reality of AI Safety Today

AI risk is not monolithic. It spans cybersecurity, data privacy, model behaviour, ethical alignment, regulatory compliance, and increasingly, physical-world and organisational harm.

As a result, AI safety platforms have evolved in silos.

1. AI Security Platforms: Necessary but Narrow

AI-native security vendors have emerged to address a rapidly growing threat surface—prompt injection, model theft, data leakage, and adversarial attacks.

Platforms such as HiddenLayer, Lakera, Mindgard, and Protect AI provide critical capabilities:

Runtime model defence
Prompt attack detection
Supply-chain security
Automated AI red teaming

Cloud security players like Wiz have also extended posture management into AI pipelines.

These platforms are essential—but they largely treat AI as another asset to secure, rather than a socio-technical system that reasons, acts, and adapts.

They answer the question:

“Is my AI being attacked?”

But not:

“Should my AI be allowed to act at all?”

2. Industrial & Workplace Safety Platforms: AI in the Physical World

In parallel, we see AI being deployed into factories, construction sites, and logistics environments—where mistakes translate into real physical harm.

Companies such as Intenseye, Invigilo, Surveily AI, and Voxel use computer vision and real-time analytics to:

Detect unsafe acts and conditions
Predict serious injury and fatality (SIF) risks
Enforce compliance in high-risk environments

These platforms demonstrate something crucial:

AI safety is not abstract. It manifests in the physical world.

Yet these systems operate almost entirely outside broader AI governance, model assurance, and regulatory compliance frameworks.

3. AI Governance & Risk Management Platforms: Policy Without Teeth

Governance platforms attempt to bring order to the chaos—mapping models, assessing risks, and aligning with emerging regulations such as the EU AI Act.

Examples include Fairly AI, Holistic AI, and Giskard, offering:

Model inventories and discovery
Risk assessments and documentation
Bias, robustness, and hallucination testing
Continuous evaluation

These tools are invaluable for compliance and assurance.

But too often, governance remains retrospective and declarative.

Policies are written. Risks are logged. Reports are generated.

What’s missing is real-time enforcement—especially when AI systems begin to act autonomously.

4. Public Sector and Open Initiatives: Standards Without Scale

Governments and large technology firms have also stepped in to shape baseline standards.

In Singapore, platforms such as AI Guardian and AI Verify have been instrumental in:

Advancing testing methodologies
Creating shared assurance language
Raising baseline trust in AI systems

Globally, initiatives like Secure AI Framework and Purple Llama push responsible practices forward.

Yet frameworks, by design, are not operating platforms.

They guide behaviour—but they do not orchestrate it.

The Missing Layer: A Unified AI Safety Control Plane

What’s absent from the market today is not another tool—but a unifying safety fabric.

A true one-stop AI Safety platform would integrate:

Security (defence against attacks)
Governance (policy, regulation, accountability)
Behavioural assurance (hallucinations, alignment, intent drift)
Operational controls (human-in-the-loop, escalation, kill-switches)
Physical-world safety (where AI actions affect humans directly)
Agent oversight (for multi-agent, goal-seeking systems)

Crucially, it would operate across the entire AI lifecycle:

design → training → deployment → runtime → evolution

Why Agentic AI Breaks Today’s Safety Model

This gap becomes existential with the rise of Agentic AI.

Agentic systems:

Decompose goals into tasks
Orchestrate tools and other agents
Learn from outcomes
Act continuously in real environments

Traditional safety assumes AI is episodic.

Agentic AI is continuous.

You cannot govern an agent with:

Annual risk assessments
Static red-team reports
PDF policies stored in SharePoint

Agentic AI requires:

Continuous monitoring
Dynamic risk scoring
Real-time intervention
Machine-enforced governance

In short:

Governance must become executable.

From “Trust by Policy” to “Trust by Design”

For years, organisations treated trust as an afterthought—something layered on once systems were live.

That approach no longer works.

In an agentic world:

Intent propagates faster than human oversight
Failures compound before detection
Responsibility becomes blurred across humans and machines

Trust must be embedded upstream, enforced midstream, and audited downstream.

This is the philosophical and architectural shift we now face.

The Road Ahead

The AI safety market is vibrant, intelligent, and rapidly evolving.

But it is still modular, disconnected, and reactive.

What’s coming next is inevitable:

A convergence of security, governance, and behavioural control
A single operational layer that treats AI systems as actors, not assets
A platform model designed for agentic, adaptive, real-world AI

That platform does not yet exist.

And until it does, we will continue to manage AI risk in fragments—while deploying systems that increasingly operate as wholes.