Techstrong TV March 16, 2026

Why It Matters

AI‑enhanced development and visibility tools are becoming essential to mitigate security risks and accelerate digital transformation, while next‑gen infrastructure ensures AI workloads run efficiently across cloud and on‑prem environments.

Key Takeaways

• AI coding assistants shift SDLC to agentic delivery model
• Google Cloud API leak underscores need for visibility tools
• Neurosymbolic AI merges language models with deterministic logic, reducing hallucinations
• Microsoft positions AI agents as interface, logic, and decision layers
• Nokia’s NetOps platform brings cloud automation to on‑prem data centers

Pulse Analysis

The rise of AI‑powered coding assistants is redefining application security practices. By embedding generative models directly into the development pipeline, organizations can automatically flag insecure code patterns and prioritize remediation based on real‑time risk scoring. This "agentic delivery" approach not only shortens remediation cycles but also addresses incidents like the Google Cloud API key exposure, where traditional static scans failed to surface misconfigurations. Integrating AI into vulnerability management equips security teams with contextual insights, turning massive alert volumes into actionable intelligence.

Beyond code, neurosymbolic AI is gaining traction as a pragmatic bridge between large language models and rule‑based systems. By coupling natural‑language understanding with deterministic logic, enterprises retain human oversight while leveraging AI to orchestrate complex workflows without the hallucination pitfalls of pure generative models. Microsoft’s roadmap amplifies this concept, positioning AI agents as the front‑end, logic layer, and decision engine across enterprise applications. Such agents can negotiate contracts, process invoices, or adjust supply‑chain parameters, delivering a unified, conversational interface that scales across departments.

Infrastructure readiness is equally critical for AI adoption. Nokia’s NetOps strategy demonstrates how Kubernetes‑native platforms and open‑source tooling can deliver cloud‑like automation within on‑prem data centers, preserving latency and compliance requirements. Complementing this, Hammerspace’s NVMe‑to‑cloud solution unifies distributed storage, using Data Assimilation and Tier‑0 pooling to keep AI workloads close to compute resources. Together, these advances ensure that as AI permeates security, retail, and broader business functions, the underlying hardware and networking layers can sustain the performance and reliability demanded by next‑generation workloads.

Original Description

AI’s Impact on AppSec and the Google Cloud API Leak: Steve Boone of Checkmarx joins Alan Shimel to discuss how AI-driven coding assistants are transforming the SDLC into an “agentic delivery lifecycle,” and why incidents like exposed Google Cloud API keys highlight the urgent need for better API visibility and AI-assisted vulnerability prioritization.

Neurosymbolic AI and the Future of Coding: Binny Gill, CEO of Kognitos, explains how neurosymbolic AI combines natural-language interfaces with deterministic logic—enabling enterprises to keep humans in control while AI agents execute complex workflows without hallucinations.

Agents + Apps + Chat: Richard Riley and Dion Hinchcliffe explore Microsoft’s vision for agentic business transformation, where AI agents serve as interfaces, logic layers and decision engines across enterprise workflows.

Reality Check Ep. 3 – AI in Retail: Dave Nicholson and Adam Gelnick examine how AI and digital platforms are reshaping retail—from the legacy of brick-and-mortar stores to competition with e-commerce giants like Amazon.

Cloud Discipline for On-Prem NetOps: Ahmed Abutaleb explains how Nokia designed a next-generation data center network using NetOps principles—leveraging Kubernetes-based platforms and open-source tooling to deliver cloud-like automation and consistency on-premises.

NVMe to Cloud with Hammerspace: Chad Smith of Hammerspace demonstrates how the platform unifies distributed storage across data centers and clouds—using capabilities like Data Assimilation and Tier-0 NVMe pooling to ensure AI workloads always access data where compute resources are available.