AI Systems Engineering
Agents, RAG pipelines, LLM integrations, and document automation, taken from idea to production. 20+ systems shipped end-to-end: self-hosted, Dockerised, and built to run every day, not to demo once.
I’m Nick Falshaw. I’ve shipped 20+ production AI systems, agents, RAG pipelines, and document automation, and I’ve spent 17+ years securing enterprise networks in banking, automotive, manufacturing, and regulated infrastructure. Building AI and securing AI are usually two hires. Here they’re one.
$ whoami Nick Falshaw, AI Engineer & AI Security Consultant $ cat focus.txt AI systems engineering AI security engineering Firewall automation at scale Cloud, zero trust & ISO 27001 $ cat creds.txt AI-102 · AZ-500 · AZ-700 · SC-500 · SC-200 · SC-100 · Terraform 004 · ISO 27001 LI · CEH · TOGAF 9 · CCIE Sec (written) $ _
Two jobs, usually at the same time: build the AI system, then secure it and prove it in audit. Seventeen years close to production sit behind that — agents and pipelines that ship, controls that survive production pressure, and the evidence for both. Firewall automation and ISO 27001 stay in the toolkit.
Agents, RAG pipelines, LLM integrations, and document automation, taken from idea to production. 20+ systems shipped end-to-end: self-hosted, Dockerised, and built to run every day, not to demo once.
Agents take action, RAG pipelines expose context, and MCP servers expand the attack surface. I threat-model agentic systems, lock down ingestion, and harden self-hosted LLM stacks before they reach production.
Vendor-agnostic change automation grounded in 280+ real migrations across Palo Alto, Check Point, Cisco, Fortinet, and F5. Rule sets that survive audit, segmentation that holds, and automation that removes the human bottleneck.
Identity-first security for Azure and hybrid estates: conditional access, segmentation, and least privilege, prioritised by the risk each control actually removes rather than the logo on the box.
ISO 27001 from gap analysis through certification, plus NIS2 and DORA readiness for regulated sectors. Passing the audit is the easy part, the goal is a programme that still holds the day something breaks.
Every claim here is backed by delivery: production systems, real migrations, and audit evidence from regulated environments. A track record you can probe before we speak.
Production firewalls, regulated audits, and AI systems shipped end-to-end, the record, not the pitch.
Building and securing AI systems: agents, RAG pipelines, and document automation on the build side, agentic threat modelling and self-hosted LLM hardening on the defence side. Firewall automation and ISO 27001 programmes for regulated estates.
Fifteen years contracting into DAX and enterprise environments, banking, automotive, manufacturing, payments and the public sector. Delivered 280+ firewall migrations and the security architecture behind them, multi-vendor and audit-ready.
Enterprise routing, switching and perimeter security, the grounding that seventeen years of firewall, compliance and now AI-security work is built on.
Three ways, and most engagements start as one of them and grow from there: a scoped AI and security review, building the system itself, or firewall and segmentation work. Fixed scope where it helps, retained where the work is ongoing.
Threat-model an AI system or audit a network estate: agents, RAG pipelines, MCP servers, firewalls, and identity. You get a prioritised findings report with fixes ranked by the risk each one removes, not a checklist.
Design and ship the AI workload end to end: agents, retrieval, and document automation, self-hosted and hardened before it reaches production. Built to run every day, with the security baked in rather than bolted on.
Vendor-agnostic firewall automation and migration grounded in 280+ real projects across Palo Alto, Check Point, Cisco, Fortinet, and F5. Rule sets that survive audit and segmentation that actually holds.
The tooling behind the work, MIT licensed: vendor playbooks and migration runbooks for Claude Code, a locked-down LLM gateway, a routing lab, and offline voice dictation.
See the reposPractical writing on firewalls, compliance, and shipping AI systems that survive real users.
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German e-invoicing for IT contractors: the €800,000 rule, why your December 2026 invoice is the first deadline, agency Gutschriften, timesheets and formats.
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Under five years bills €83.41, over twenty bills €106.81, and years five to ten are worth €1.44. Why the stable rate headline hides the number that actually moved.
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Top-20 growth halved to 4.1%, six firms shrank and one grew 26.4%. A 45-point spread in a flat market, and the detail that undercuts the industry’s own AI pitch.
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85% of German firms call themselves too dependent on US cloud, 71% still buy it and only 8% prefer it. Why that gap is an untested exit cost, and the two-week test that turns it into a number.
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Three independent 2026 studies found 95% of enterprise AI pilots deliver no measurable return. The model is rarely the reason. Where they actually die is the security review, and the 80 percent of the work nobody scoped.
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The biggest open-weight model ever topped the leaderboards and omitted a 51% hallucination rate. Why benchmark theater is compliance theater, and how to evaluate AI models the way an auditor evaluates controls.
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The first ransomware run entirely by an AI agent, foothold to encryption, no operator. It went from failed login to working fix in 31 seconds. What breaks when the adversary's tempo and cost both collapse.
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A researcher found Claude Code quietly fingerprinting users and phoning markers home. The scandal is not Anthropic. It is that you gave a vendor agent root on your codebase and never audited the trust.
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The unread access review, the log-only WAF, the phishing-completion metric, the rubber-stamp firewall review, the regex "guardrail". Controls that pass audit and stop nothing, and how to test effectiveness instead of existence.
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16 billion credentials leaked and stolen sessions walk past MFA. Possession stopped proving identity. The fix is continuous verification, at the firewall and the AI agent alike.
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AI agents are non-human identities with standing access. After 17 years cleaning up orphaned firewall rules, the sprawl, and the cure, are familiar.
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An AI agent ran a real intrusion end to end, foothold to data exfiltration, in under an hour with no human. What it breaks for defenders.
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A Check Point VPN zero-day and the BadHost flaw in Starlette are the same CWE-287 mistake, one stack apart: trusting attacker-controlled input to authenticate.
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For 17 years I scored security risk in CVSS. Anthropic just gave AI agents their own number: a 31.5% prompt-injection hijack rate. The risk discipline transfers.
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Prompt injection is a confused-deputy attack: the agent spends its own authority for an attacker. Network security solved that with trust boundaries, not smarter parsing.
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A root-RCE firewall CVE and a poisoned AI-agent skill marketplace share one root cause: no provenance, no least privilege, no change control.
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Where production LLM systems break first, based on mapping the OWASP LLM Top 10 against real codebases.
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Zero Trust for the Mittelstand, not the Fortune 500: identity, segmentation, and continuous verification without a full rebuild.
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The backbone of Europe’s economy built its advantage through engineering, not security. Here is what BSI deadlines require and how to close the gap.
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Hundreds of assessments across TISAX, PCI-DSS, ISO 27001, and NIS2, plus the lessons that do not appear in any framework.
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How to reduce alert noise, find novel attacker behaviour, and turn detection maturity into evidence the board can understand.
ReadThe method stays consistent whether I’m building an AI system or securing one: evidence first, architecture second, implementation third, verification always.
Map what’s really deployed, topology, AI workloads, identity, threat surface, regulatory scope. Facts, not assumptions.
Design the target state and the path to it. AI controls and network defences on one blueprint, prioritised by risk and effort.
Build it: firewall automation, RAG controls, identity, and segmentation hardened against the vectors that actually land.
Prove it under real load. Useful alerting, owned runbooks, and audit evidence produced as part of delivery.
Building an AI system? Running one that’s never been threat-modelled? Still on firewalls and ISO 27001? Tell me what you’re building or defending and what deadline matters. I reply within one business day.
You’ll get a real reply from me within one business day.