AI in Security: Defence for All!

Is it safe to say that we live securely? If not, what can we do to make things safer? Does this apply only to our homes, or are there things that we can do for ourselves? And what about homeland security? The answer lies inside!

AI in Security: Defence for All!
Written by TechnoLynx Published on 06 Jan 2025

Introduction

We do many things daily. We prepare breakfast, make our bed, go to work, spend time with friends, do our hobbies. There is one thought, however, that rarely crosses our minds. ‘Am I safe?’ Mostly, yes, but can this be improved? Has security maybe been neglected lately? Are the systems that we have enough, or do they need modernisation? Can AI help in their improvement, and how? The answers to all these questions are given in this article!

That’s My Safe Place

There is no place like home. Even if you are on vacation in the most beautiful place on Earth or you are simply returning from work, home is where you actually relax and rest. We really value our homes. In fact, the market size of the home improvement industry in the United States has jumped from 294.5 billion dollars to 549.2 billion dollars in 2024, with an expected increase to 602.5 billion dollars in 2027. At the same time, in 2020, 35% of DIY projects focused on interior repainting, followed by bathroom remodelling at 31%. The top 3 are complete with new flooring installation at 26% (Statista - The Statistics Portal). Since the beginning of our history, people have always tried to improve their living spaces, either with decorative alterations or with functional ones. Focussing on the latter, there is one category that is often neglected, maybe because we do not keep it in mind, or maybe we have in mind that ‘it’s not going to happen to us’. This is no other than home security. Let’s see!

Figure 1 – Home service projects in the USA, 2020 (Statista, n.d.)
Figure 1 – Home service projects in the USA, 2020 (Statista, n.d.)

In the old days, people were much more consistent with home security, using drawbridges, guard dogs, or even traps on some occasions. As our civilisation progressed and we started having rules, it was decided that it would be better if we were guarded by dedicated groups of people (aka the police), and these methods of the past became kind of obsolete. Due to different factors, these dedicated groups of people have been shown to be ineffective in some circumstances, mainly due to understaffing or funds being redirected to other needs. Burglars are the ideal candidates to take advantage of this situation, and history has shown that they can become very creative! This is where an advanced home security system can shine. And yes, there are already some ‘advanced’ systems out there, but they are not as capable as they could be. Let us explain.

A home security system usually consists of some sensors placed on door frames and windows that are connected to a central unit placed somewhere inside the house. This unit is connected to a siren on the exterior, which rings when a security breach has been made, and it also notifies the police through a subscription plan if desired. However, due to ineffectiveness, though, there is no guarantee that the police will be able to reach you in time, which, in turn, does not guarantee your safety or the identification of the burglars. Surely enough, there are also systems that are equipped with CCTV, but these cannot guarantee identification in case the burglars are masked. This is where AI and the all-powerful GPU-accelerated Computer Vision (CV) come to aid. One of the advantages of an AI-powered CV system is that it can learn and adapt as it goes; plus, there is no need for anyone to show their face. Identification can include body language, which can be really as unique as a fingerprint at times, walking patterns, or even the tools that are being used. Although a properly trained CV model can guarantee excellent results, these results, of course, will not and can’t be generated from one single instance. Imagine having a network of Internet of Things (IoT) home security systems enhanced by computer vision (CV) and integrated with CCTV cameras, instantly collecting and comparing data of all cases of home theft. Edge Computing can make sure of proper data transfer without losses. They could analyse patterns and behaviours, perform threat analysis, even collect data, and give crucial information about the wider area of the thefts, potentially even forecasting where the burglars will hit next!

Figure 2 – Computer Vision-powered CCTV detecting a burglar in real-time through segmentation in a residential building ( SuperAnnotate, n.d.)
Figure 2 – Computer Vision-powered CCTV detecting a burglar in real-time through segmentation in a residential building ( SuperAnnotate, n.d.)

Read more: Understanding Computer Vision and Pattern Recognition

All for One…

Home security systems are surely great to have, but can we really rely on them only? What about ourselves? What happens when we are not at home? There are many ways to counter potential threats. There are different self-defence ‘tools’, of course, but they are not always legal, depending on the country or state where you live. However, one thing is always legal, and that is self-defence training.

Martial arts have been around since forever, and apart from training the body, their goal is also to train the mind. There are approximately 180 different styles of martial arts, with many of them having even substyles, such as Karate and Kung Fu. Most martial arts originate from the East, but plenty of them have been redesigned or even involved in their own thing in the West. Brazilian Jiu-Jitsu, for example, has differentiated itself from traditional Jiu-Jitsu, while boxing is a category on its own. Surely, even though all of them share the same goal, training, the endgame can differ, as some are attack-focused, while others are purely defensive or even solely spiritual, focusing mostly on the health benefits through meditation and gentle exercise, such as Tai Chi.

Although training usually takes place collectively in sports clubs, there are people who, for their own reasons, prefer to train solo. Nothing wrong with that, if you ask us, but the competition is kind of lost in this way, and with competition comes improvement; plus, human interaction is key to martial arts. Keep in mind that there is a reason we bow before starting our training, and that is that we show our respect to our opponent for lending us his body to train with it. To quote the much-loved Mr. Miyagi: ‘Karate? Learn from a book?!’.

Figure 3 – AI-generated photo of a black belt martial artist (Vecteezy, n.d.)
Figure 3 – AI-generated photo of a black belt martial artist (Vecteezy, n.d.)

We have seen the revolution Extended Reality (XR) has brought in many different fields. Its subbranches, Augmented Reality (AR) and Virtual Reality (VR) can be beneficial to solo practitioners as well. An XR headset can place you in any virtual environment you want, isolating you from anything that can distract you and really setting you in the mood. Do you want to train your katas? Easy. Do you want to spar with opponents? Say no more! Generative AI can analyse data from thousands of professionals, giving you as many or as few opponents as you want. The level can be adjusted according to your own. Match that with an AI-powered camera (or more) and even a capable smartwatch; you can build your own dojo at home with real-time monitoring of your performance stance; you could even receive hints and advice on how to become better. If you want to take this system even further, turn it into your personal martial arts master. Natural Language Processing (NLP) can literally transform how you interact with a machine. Now, reading information is one thing, but wouldn’t you rather hear it instead? With Generative AI, you can get as close to regular practice or a real-world scenario as possible!

…And One for All

We have seen how AI can enhance security on a personal or even a household level, but what happens when we want to protect something more collective? Let’s say an entire nation! Countries spend a lot of money on their defence programmes. The defence industry is worth billions of dollars. For some, it is an actual duty towards the homeland, but for others, it is just business. Whatever the reason might be, it is beyond the scope of this article. However, it is worth taking a look at roughly how nations manage to defend their territory and check how AI is implemented in their strategy.

Each nation that has a tactical military force usually divides it into 3 major branches: the army, the navy, and the air force, each of which operates in the land, sea, and air, respectively, with some interdisciplinary peculiarities, of course. However, all units in these branches are using some kind of equipment, be it firearms, rifles, or vehicles, all of which can be enhanced using AI. We can have scopes that are able to precisely calculate the distance between the soldier and the target through CV, vehicles that share coordinates among them when making a formation on the field, magazines that are connected to smart helmets with Head-Up Displays (HUDs), demonstrating the vitals and all necessary information a soldier needs on the combat field. Focussing on the latter, have you wondered what the one thing that unites all personnel of all branches is? That’s right, the uniform! Army Combat Uniforms (ACUs) are made to certain standards, but you have to admit that they are General Issue (GI), which means they follow the one-to-fit-all guidelines.

Figure 4 – Exoskeleton-powered ‘super soldier’ opposing a traditional soldier (Here, 2021)
Figure 4 – Exoskeleton-powered ‘super soldier’ opposing a traditional soldier (Here, 2021)

Despite that, some countries have actually spent a lot of money on R&D to revolutionise their ACUs. The US military has been developing the Tactical Assault Light Operator Suit (TALOS), a new form of bulletproof ACU with a powered exoskeleton (Iron Man suit for US military - thanks to Hollywood customers, 2014). There were some rumours that it had adaptive camouflage with CV use, but we are not buying it just yet. Take that and pair it with the AR smart helmet we said before, sensors all over the suit, a CV-enhanced scope on a standard rifle, and you have your ultimate fighter. Unfortunately, the programme was declared non-feasible, and its development stopped in February 2019. Moving to the other side of the Atlantic (or the Pacific, have it your way), the Ratnik program has started in Russia since 2015 (Ellmer, 2022). This programme is mostly soldier-orientated, as its goal is to improve the connectivity and effectiveness of combat personnel. The improvements include modernised body armour with enhanced body heat coverage, special optics, and communications. CV for optics, low heat signal, which combines readings from the entire squad for maximum adaptability, and the possibility of IoT for communications.

As they say, ‘battles are won by men’, and it doesn’t really matter if you are the best soldier or not. Whoever is on the battlefield has needs, and one of the biggest issues is reloading. The ability to constantly refuel soldiers with ammunition can be a game changer, as you understand. Remember the smart magazines we mentioned above? Sending information directly to the HUD is one thing, but imagine that on a larger scale. No need to shout ‘reload’ or send a message to anyone. IoT solutions can make it happen so that the ammunition reaches you before you even say, ‘I’m out!’

Summing Up

In this article, we barely scratched the surface of how AI can be implemented in the field of security, both personal and collective, but we discovered many things! We saw how Computer Vision and the Internet of Things can transform home security, how you can train yourself to be a fighter with the help of Computer Vision and Virtual Reality, and how the defence programmes of nations can transform their effectiveness on the battlefield. There is no doubt that applying AI in security can benefit us, and the options are truly endless!

What We Offer

At TechnoLynx, we know what innovation is. We specialise in finding custom-tailored solutions for all your needs. The benefits of integrating AI into security applications are something we understand and value more than anyone. It is our commitment to provide cutting-edge solutions while ensuring safety in human-machine interactions, managing and analysing large data sets, and addressing ethical considerations.

We provide precise software solutions that empower many fields and industries using AI-driven algorithms. Innovation is our commitment, and we are driven to adapt to the ever-evolving AI landscape. We present solutions designed to increase accuracy, efficiency, and productivity. eel free to contact us, share your ideas or questions, and rest assured that we will make your project fly!

Continue reading: Ensuring Security in Video Conferencing Solutions

List of references

Cost, Efficiency, and Value Are Not the Same Metric

Cost, Efficiency, and Value Are Not the Same Metric

17/04/2026

Performance per dollar. Tokens per watt. Cost per request. These sound like the same thing said differently, but they measure genuinely different dimensions of AI infrastructure economics. Conflating them leads to infrastructure decisions that optimize for the wrong objective.

Precision Is an Economic Lever in Inference Systems

Precision Is an Economic Lever in Inference Systems

17/04/2026

Precision isn't just a numerical setting — it's an economic one. Choosing FP8 over BF16, or INT8 over FP16, changes throughput, latency, memory footprint, and power draw simultaneously. For inference at scale, these changes compound into significant cost differences.

Precision Choices Are Constrained by Hardware Architecture

Precision Choices Are Constrained by Hardware Architecture

17/04/2026

You can't run FP8 inference on hardware that doesn't have FP8 tensor cores. Precision format decisions are conditional on the accelerator's architecture — its tensor core generation, native format support, and the efficiency penalties for unsupported formats.

Steady-State Performance, Cost, and Capacity Planning

Steady-State Performance, Cost, and Capacity Planning

17/04/2026

Capacity planning built on peak performance numbers over-provisions or under-delivers. Real infrastructure sizing requires steady-state throughput — the predictable, sustained output the system actually delivers over hours and days, not the number it hit in the first five minutes.

How Benchmark Context Gets Lost in Procurement

How Benchmark Context Gets Lost in Procurement

16/04/2026

A benchmark result starts with full context — workload, software stack, measurement conditions. By the time it reaches a procurement deck, all that context is gone. The failure mode is not wrong benchmarks but context loss during propagation.

Building an Audit Trail: Benchmarks as Evidence for Governance and Risk

Building an Audit Trail: Benchmarks as Evidence for Governance and Risk

16/04/2026

High-value AI hardware decisions need traceable evidence, not slide-deck bullet points. When benchmarks are documented with methodology, assumptions, and limitations, they become auditable institutional evidence — defensible under scrutiny and revisitable when conditions change.

The Comparability Protocol: Why Benchmark Methodology Defines What You Can Compare

The Comparability Protocol: Why Benchmark Methodology Defines What You Can Compare

16/04/2026

Two benchmark scores can only be compared if they share a declared methodology — the same workload, precision, measurement protocol, and reporting conditions. Without that contract, the comparison is arithmetic on numbers of unknown provenance.

A Decision Framework for Choosing AI Hardware

A Decision Framework for Choosing AI Hardware

16/04/2026

Hardware selection is a multivariate decision under uncertainty — not a score comparison. This framework walks through the steps: defining the decision, matching evaluation to deployment, measuring what predicts production, preserving tradeoffs, and building a repeatable process.

How Benchmarks Shape Organizations Before Anyone Reads the Score

How Benchmarks Shape Organizations Before Anyone Reads the Score

16/04/2026

Before a benchmark score informs a purchase, it has already shaped what gets optimized, what gets reported, and what the organization considers important. Benchmarks function as decision infrastructure — and that influence deserves more scrutiny than the number itself.

Accuracy Loss from Lower Precision Is Task‑Dependent

Accuracy Loss from Lower Precision Is Task‑Dependent

16/04/2026

Reduced precision does not produce a uniform accuracy penalty. Sensitivity depends on the task, the metric, and the evaluation setup — and accuracy impact cannot be assumed without measurement.

Precision Is a Design Parameter, Not a Quality Compromise

Precision Is a Design Parameter, Not a Quality Compromise

16/04/2026

Numerical precision is an explicit design parameter in AI systems, not a moral downgrade in quality. This article reframes precision as a representation choice with intentional trade-offs, not a concession made reluctantly.

Mixed Precision Works by Exploiting Numerical Tolerance

Mixed Precision Works by Exploiting Numerical Tolerance

16/04/2026

Not every multiplication deserves 32 bits. Mixed precision works because neural network computations have uneven numerical sensitivity — some operations tolerate aggressive precision reduction, others don't — and the performance gains come from telling them apart.

Throughput vs Latency: Choosing the Wrong Optimization Target

16/04/2026

Throughput and latency are different objectives that often compete for the same resources. This article explains the trade-off, why batch size reshapes behavior, and why percentiles matter more than averages in latency-sensitive systems.

Quantization Is Controlled Approximation, Not Model Damage

16/04/2026

When someone says 'quantize the model,' the instinct is to hear 'degrade the model.' That framing is wrong. Quantization is controlled numerical approximation — a deliberate engineering trade-off with bounded, measurable error characteristics — not an act of destruction.

GPU Utilization Is Not Performance

15/04/2026

The utilization percentage in nvidia-smi reports kernel scheduling activity, not efficiency or throughput. This article explains the metric's exact definition, why it routinely misleads in both directions, and what to pair it with for accurate performance reads.

FP8, FP16, and BF16 Represent Different Operating Regimes

15/04/2026

FP8 is not just 'half of FP16.' Each numerical format encodes a different set of assumptions about range, precision, and risk tolerance. Choosing between them means choosing operating regimes — different trade-offs between throughput, numerical stability, and what the hardware can actually accelerate.

Peak Performance vs Steady‑State Performance in AI

15/04/2026

AI systems rarely operate at peak. This article defines the peak vs. steady-state distinction, explains when each regime applies, and shows why evaluations that capture only peak conditions mischaracterize real-world throughput.

The Software Stack Is a First‑Class Performance Component

15/04/2026

Drivers, runtimes, frameworks, and libraries define the execution path that determines GPU throughput. This article traces how each software layer introduces real performance ceilings and why version-level detail must be explicit in any credible comparison.

The Mythology of 100% GPU Utilization

15/04/2026

Is 100% GPU utilization bad? Will it damage the hardware? Should you be worried? For datacenter AI workloads, sustained high utilization is normal — and the anxiety around it usually reflects gaming-era intuitions that don't apply.

Why Benchmarks Fail to Match Real AI Workloads

15/04/2026

The word 'realistic' gets attached to benchmarks freely, but real AI workloads have properties that synthetic benchmarks structurally omit: variable request patterns, queuing dynamics, mixed operations, and workload shapes that change the hardware's operating regime.

Why Identical GPUs Often Perform Differently

15/04/2026

'Same GPU' does not imply the same performance. This article explains why system configuration, software versions, and execution context routinely outweigh nominal hardware identity.

Training and Inference Are Fundamentally Different Workloads

15/04/2026

A GPU that excels at training may disappoint at inference, and vice versa. Training and inference stress different system components, follow different scaling rules, and demand different optimization strategies. Treating them as interchangeable is a design error.

Performance Ownership Spans Hardware and Software Teams

15/04/2026

When an AI workload underperforms, attribution is the first casualty. Hardware blames software. Software blames hardware. The actual problem lives in the gap between them — and no single team owns that gap.

Performance Emerges from the Hardware × Software Stack

15/04/2026

AI performance is an emergent property of hardware, software, and workload operating together. This article explains why outcomes cannot be attributed to hardware alone and why the stack is the true unit of performance.

Power, Thermals, and the Hidden Governors of Performance

14/04/2026

Every GPU has a physical ceiling that sits below its theoretical peak. Power limits, thermal throttling, and transient boost clocks mean that the performance you read on the spec sheet is not the performance the hardware sustains. The physics always wins.

Why AI Performance Changes Over Time

14/04/2026

That impressive throughput number from the first five minutes of a training run? It probably won't hold. AI workload performance shifts over time due to warmup effects, thermal dynamics, scheduling changes, and memory pressure. Understanding why is the first step toward trustworthy measurement.

CUDA, Frameworks, and Ecosystem Lock-In

14/04/2026

Why is it so hard to switch away from CUDA? Because the lock-in isn't in the API — it's in the ecosystem. Libraries, tooling, community knowledge, and years of optimization create switching costs that no hardware swap alone can overcome.

GPUs Are Part of a Larger System

14/04/2026

CPU overhead, memory bandwidth, PCIe topology, and host-side scheduling routinely limit what a GPU can deliver — even when the accelerator itself has headroom. This article maps the non-GPU bottlenecks that determine real AI throughput.

Why AI Performance Must Be Measured Under Representative Workloads

14/04/2026

Spec sheets, leaderboards, and vendor numbers cannot substitute for empirical measurement under your own workload and stack. Defensible performance conclusions require representative execution — not estimates, not extrapolations.

Low GPU Utilization: Where the Real Bottlenecks Hide

14/04/2026

When GPU utilization drops below expectations, the cause usually isn't the GPU itself. This article traces common bottleneck patterns — host-side stalls, memory-bandwidth limits, pipeline bubbles — that create the illusion of idle hardware.

Why GPU Performance Is Not a Single Number

14/04/2026

AI GPU performance is multi-dimensional and workload-dependent. This article explains why scalar rankings collapse incompatible objectives and why 'best GPU' questions are structurally underspecified.

What a GPU Benchmark Actually Measures

14/04/2026

A benchmark result is not a hardware measurement — it is an execution measurement. The GPU, the software stack, and the workload all contribute to the number. Reading it correctly requires knowing which parts of the system shaped the outcome.

Why Spec‑Sheet Benchmarking Fails for AI

14/04/2026

GPU spec sheets describe theoretical limits. This article explains why real AI performance is an execution property shaped by workload, software, and sustained system behavior.

Visual Computing in Life Sciences: Real-Time Insights

6/11/2025

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AI-Driven Aseptic Operations: Eliminating Contamination

21/10/2025

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AI Visual Quality Control: Assuring Safe Pharma Packaging

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Barcodes in Pharma: From DSCSA to FMD in Practice

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Pharma’s EU AI Act Playbook: GxP‑Ready Steps

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A clear, GxP‑ready guide to the EU AI Act for pharma and medical devices: risk tiers, GPAI, codes of practice, governance, and audit‑ready execution.

Cell Painting: Fixing Batch Effects for Reliable HCS

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Reduce batch effects in Cell Painting. Standardise assays, adopt OME‑Zarr, and apply robust harmonisation to make high‑content screening reproducible.

Explainable Digital Pathology: QC that Scales

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Raise slide quality and trust in AI for digital pathology with robust WSI validation, automated QC, and explainable outputs that fit clinical workflows.

Validation‑Ready AI for GxP Operations in Pharma

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Make AI systems validation‑ready across GxP. GMP, GCP and GLP. Build secure, audit‑ready workflows for data integrity, manufacturing and clinical trials.

Edge Imaging for Reliable Cell and Gene Therapy

17/09/2025

Edge imaging transforms cell & gene therapy manufacturing with real‑time monitoring, risk‑based control and Annex 1 compliance for safer, faster production.

AI in Genetic Variant Interpretation: From Data to Meaning

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AI Visual Inspection for Sterile Injectables

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