Industry Trends blog posts(38)
Banking on resilience in the AI era: Why 2026 is different
In 2026, resilience is a top banking priority. Discover why outages, digital sovereignty, and AI in production make always‑on infrastructure a key differentiator.
By Chad Davis
Operational sovereignty: Why digital portability drives resilience
Learn how operational sovereignty boosts resilience and why having the right tools is critical to staying ahead in an unpredictable world.
By Bart Salaets
AI observability: Auditing and tracing AI decisions
Discover how AI observability ensures accountability for AI decisions with advanced runtime visibility, auditing, & traceability across AI workflows & systems.
By Jessica Brennan
From packets to prompts: Inference adds a new layer to the stack
Inference is not training. It is not experimentation. It is not a data science exercise. Inference is production runtime behavior, and it behaves like an application tier.
By Lori Mac Vittie
Secure your AI data pipeline without slowing pipelines down
Securing the AI data pipeline is not optional. That much is clear. What is far less obvious—and far more challenging—is how to secure it well
By Mark Menger
Why BOLA’s "authorization gap" requires a runtime strategy
Broken object-level authorization (BOLA) is not a bug but a failure of access controls. Learn how a three-tiered runtime strategy can close the gap and protect data.
By Chaim Peer, Ian Dinno
AI compliance and regulation: Using F5 AI Guardrails to meet legal and industry standards
See how F5 AI Guardrails and F5 AI Red Team simplify compliance, reduce legal exposure, and enable organizations to demonstrate responsible AI governance.
By Jessica Brennan
Delivering AI applications at scale: The role of ADCs
Delivering AI applications reliably requires more than fast models or elastic infrastructure. It requires a control layer to govern how AI apps are consumed.
By Buu Lam
Compression isn’t about speed anymore, it’s about the cost of thinking
In the AI era, compression reduces the cost of thinking—not just bandwidth. Learn how prompt, output, and model compression control expenses in AI inference.
By Lori Mac Vittie
Classifier-based vs. LLM-driven guardrails: What actually works at AI runtime
Not all AI guardrails work the same way. Learn why it’s important to know the distinction between classifier-based and LLM-driven guardrails.
By Jessica Brennan
MCP: The key to AI-ready application delivery
Model Context Protocol (MCP) enables secure, low-latency AI integration in modern applications. Learn why MCP is essential for future-proof application delivery strategies.
By Griff Shelley
Responsible AI: Guardrails align innovation with ethics
AI innovation moves fast. But without the right guardrails, speed can come at the cost of trust, accountability, and long-term value.
By Mark TolerBest practices for optimizing AI infrastructure at scale
Optimizing AI infrastructure isn’t about chasing peak performance benchmarks. It’s about designing for stability, resiliency, security, and operational clarity
By Mark Menger
Why your AI policy, governance, and guardrails can’t wait
AI policy and governance can no longer be treated as paperwork exercises or future considerations. They must be paired with practical, enforceable guardrails.
By Mark Toler
AI risk management: how guardrails can offer mitigation
AI risk is driven by probabilistic behavior and limited explainability, not just security flaws. Effective management requires continuous operational control.
By Ian Lauth
AI data privacy: guardrails that protect sensitive data
AI data privacy breaches have a direct impact on customer trust and reinforce data privacy as not just a technical challenge, but also as a governance and trust issue.
By Ian LauthWhy AI storage demands a new approach to load balancing
AI workloads require reimagined storage. Their distinct data access patterns reveal critical bottlenecks and deep inefficiencies within legacy infrastructure.
By Mark Menger, Griff Shelley
The efficiency trap: tokens, TOON, and the real availability question
Token efficiency in AI is trending, but at what cost? Explore the balance of performance, reliability, and correctness in formats like TOON and natural-language templates.
By Lori Mac Vittie
What are AI guardrails? Evolving safety beyond foundational model providers
See why AI guardrails must evolve to address a more holistic picture of risk within organizations’ rapidly evolving threat surfaces.
By Ian Lauth, James White
The trust imperative for agentic AI in financial services
Learn how agentic AI is reshaping the financial services industry—and the technical and governance challenges that must be overcome.
By Chad Davis
Datos Insights: Securing APIs and multicloud in financial services
New threat analysis from Datos Insights highlights actionable recommendations for API and web application security in the financial services sector
By Chad DavisTracking AI data pipelines from ingestion to delivery
Enterprise data must pass through ingestion, transformation, and delivery to become training-ready. Each stage has to perform well for AI models to succeed.
By Ahmed Dessouki, Mark Menger, Joe Kanagusuku
10 tips for starting your PQC journey today
Getting started on PQC readiness can be difficult. You can’t protect what you can’t see, and you can’t migrate what you haven’t mapped. Here are helpful tips.
By Chuck Herrin
Secrets to scaling AI-ready, secure SaaS
Learn how secure SaaS scales with application delivery, security, observability, and XOps.
By Mani GaddeOptimizing AI pipelines by removing bottlenecks in modern workloads
As AI workloads scale, organizations are discovering slowdowns that come from the upstream data pipeline that feeds the AI model. Here's how F5 BIG-IP can help.
By Hunter Smit
Why SASE and ADSP are complementary platforms
SASE secures the hybrid workforce and ADSP secures the hybrid application estate. Learn how these converged platforms differ and why they complement each other.
By John Maddison
Programmability is the only way control survives at AI scale
Learn why data and control plane programmability are crucial for scaling and securing AI-driven systems, ensuring real-time control in a fast-changing environment.
By Lori Mac Vittie
The top five tech trends to watch in 2026
Explore the top tech trends of 2026, where inference dominates AI, from cost centers and edge deployment to governance, IaaS, and agentic AI interaction loops.
By Lori Mac Vittie
The future of eCommerce is agentic AI
Retailers deploying agentic AI into their eCommerce platforms need to adapt risk strategies and security defenses to properly govern autonomous agents.
By Mani Gadde
Fueling the AI data pipeline with F5 and S3-compatible storage
When S3-compatible storage is paired with F5 BIG-IP, organizations can ensure their AI data pipelines stay resilient, secure, and always flowing at peak performance.
By Hunter Smit