The Scale Problem No One Saw Coming

Smart computing systems have evolved beyond anyone's initial expectations. What started as algorithms making simple recommendations has transformed into autonomous systems making millions of trust decisions per second, decisions about access, permissions, and authority that directly impact security and operations.

And here's the uncomfortable truth: we're still trying to govern these systems with approaches designed for a much simpler era.

"We built smart systems," explains Mr.Nandagopal Seshagiri, a Senior Software Cybersecurity Architect whose two-decade career has been dedicated to solving the intersection of scale and security, "but we're still trying to govern them using mechanisms that were never designed for this level of autonomy or scale."

Seshagiri has spent years architecting identity and access management systems for some of the world's most demanding environments where millions of trust decisions happen every second, and a single governance failure can cascade across entire infrastructures. His perspective comes not from theoretical frameworks, but from wrestling with the practical realities of making large-scale systems both intelligent and trustworthy.

This isn't just a theoretical concern. As AI-driven services, autonomous infrastructure, and machine-to-machine interactions proliferate across industries, the gap between system capability and governance capacity is widening dangerously.

When Human + Rules Isn't Enough Anymore

For decades, trust in computing rested on two pillars: human judgment and deterministic rules.

Humans brought contextual understanding and nuanced decision-making. Hand-programmed systems provided consistency and speed through predefined logic.

This worked until it didn't.

The problem? Modern systems operate at scales and speeds that make traditional governance impractical.

Consider the math: A typical enterprise cloud environment might process millions of access requests daily. Each request involves multiple trust decisions, identity verification, permission validation, context evaluation, and risk assessment.

No human team can review that volume. Even if they could, real-time systems can't wait for human approval.

Meanwhile, static rule-based systems carry their own fatal flaw.

"Hand-programmed systems tend to fail silently," Seshagiri points out. "They continue making decisions confidently, even when the assumptions they were built on are no longer valid."

A firewall rule written six months ago doesn't know that your threat landscape changed yesterday. A permission policy designed for on-premises infrastructure doesn't understand the nuances of multi-cloud zero-trust architectures.

The rules keep running. The decisions keep happening. But the foundation underneath has shifted.

Trust Is No Longer Static

Here's what's fundamentally different about smart computing environments:

• Traditional systems: Establish trust once, assume it persists
• Smart systems: Trust must be continuously computed

In modern architectures, you're not dealing with isolated transactions anymore. You have:

• Users accessing services across multiple contexts
• AI agents acting with delegated authority
• Microservices making machine-to-machine requests
• Infrastructure components self-healing and auto-scaling
• Third-party integrations operating within your perimeter

Each interaction requires a trust decision. But binary allow/deny logic falls short.

"Trust is no longer something you establish once and assume forever," Seshagiri emphasizes. "In smart systems, trust has to be continuously computed."

What matters now isn't just who is requesting access, but:

• Confidence level in the identity assertion
• Risk signals from behavior and context
• Historical patterns and anomaly detection
• Real-time threat intelligence

Traditional governance models weren't built to process this complexity at scale.

Enter AI-Mediated Trust Decisions

So what's the answer?

Seshagiri and other security architects are exploring what he calls AI-mediated trust decisions using artificial intelligence as an adaptive decision layer rather than a replacement for human judgment.

"Humans approving access for AI at machine speed simply doesn't work," Seshagiri observes. "You either slow the system to a halt or train people to approve requests without real scrutiny."

The alternative, he argues, is not more privilege or more approvals, but Zero Trust applied to AI itself.

"Zero Trust taught us to stop assuming trust," Seshagiri explains. "AI-mediated trust is about deciding trust continuously, at machine speed, without overwhelming humans or hard-coded systems."

Here's how it works in practice:

• Low-confidence scenarios: System evaluates multiple signals, identity assertions, behavioral patterns, contextual factors, and risk indicators. For routine, low-risk actions where confidence is high, decisions happen automatically.
• Medium-confidence scenarios: When uncertainty exists, the system introduces adaptive challenges or additional verification steps not because rules require it, but because confidence levels suggest it.
• High-risk scenarios: When confidence drops below safe thresholds, decisions escalate to human oversight.

"This isn't about giving AI unchecked authority," Seshagiri clarifies. "It's about teaching systems when they can decide safely, and when they shouldn't decide alone."

The key insight: Trust decisions become probabilistic rather than deterministic.

Instead of "Does this request match our rules?" the question becomes "How confident are we that this request is legitimate and safe?"

This shift enables systems to handle scale while maintaining security rigor.

The Tri-Modal Governance Model

Rather than positioning this as humans versus machines, Seshagiri advocates for what he calls a collaborative, tri-modal model:

1. Deterministic Systems provide compliance guardrails and non-negotiable boundaries
2. AI Systems handle scale, adaptation, and probabilistic decision-making
3. Human Oversight intervenes selectively where judgment and accountability are essential

"The future isn't AI replacing people or rules replacing reasoning," Seshagiri explains. "It's about aligning the strengths of each so that trust decisions remain scalable, explainable, and governable."

Think of it like driving:

• The road itself (deterministic rules) defines boundaries you can't cross
• Cruise control and lane assist (AI systems) handle routine decisions at scale
• The driver (human oversight) maintains ultimate authority and handles edge cases

Each layer serves a purpose. The magic happens when they work together.

The Hard Problems Ahead

This approach isn't without challenges. Seshagiri acknowledges several critical areas that remain active research domains:

• Explainability: When AI systems make trust decisions, stakeholders need to understand why Black-box decision-making is incompatible with governance requirements in regulated industries.
• Adversarial Manipulation: Attackers will inevitably attempt to game AI-based trust systems feeding false signals, poisoning training data, or exploiting edge cases in decision logic.
• Long-term Accountability: When AI-mediated decisions go wrong, who's responsible? The architect who designed the system? The team that trained the models? The organization deploying it?

These aren't just technical problems; they're governance, legal, and ethical questions that the industry must address collectively.

Why This Matters Now

As smart computing expands into critical domains, infrastructure, healthcare, finance, public services, the way trust decisions are made directly impacts both system reliability and public confidence.

Get it wrong, and you either:

• Over-restrict access, creating friction that degrades user experience and operational efficiency
• Under-restrict access, creating security gaps that adversaries will exploit

The stakes are too high for either extreme.

"Smart systems aren't limited by compute anymore," Seshagiri concludes. "They're limited by how trust is decided."

The next phase of smart computing won't be defined just by how intelligent our systems become but by how wisely that intelligence is governed.

"The views and opinions expressed in this article are solely my own and do not necessarily reflect those of any affiliated organizations or entities."