Stallion Integrated, as part of the Stallion Group, is redefining the security landscape. Founded in 1991 as a guarding company, Stallion has evolved into a Level 1 BBBEE provider of integrated business solutions, combining people, technology, and innovation. By harnessing artificial intelligence (AI), machine learning, and Internet of Things (IoT) technologies, the company is delivering measurable improvements in detection accuracy, response times, and decision-making, while ensuring that human guards remain at the centre of security delivery.

Overseeing more than 300 sites and 3,700 cameras nationwide, Stallion’s control rooms utilise AI to monitor high-risk activities, including perimeter breaches, unauthorised vehicle access, and loitering. Moreover, the system learns each site’s unique patterns, from staff movements to deliveries, to filter out false positives. This has reduced nuisance alarms by almost 98%, enabling controllers to act only on genuine threats.

“The implementation of AI isn’t here to replace guards, but to make them safer and more effective,” said Riaan Willemse, GM: Operations & IT at Stallion Integrated. “By filtering nuisance alarms, our teams can focus on real threats, whether it’s suspicious movement along a fence or unusual vehicle activity at access points.”

The integration is seamless, with AI overlaying onto existing CCTV systems and transforming standard cameras into smart analytics tools. All activity, including alarms, patrols, incidents, and CCTV, is consolidated into an Electronic Occurrence Book, providing clients with a single, transparent reporting platform. Furthermore, guards actively conduct tests to ensure system integrity, allowing them to respond with greater situational awareness. Beyond intrusions, Stallion’s systems also detect tailgating, fire, smoke, and flooding.

The technology has shown a tangible impact across industrial sites, logistics hubs, residential estates, and retail centres. Licence plate recognition, linked to national databases, has led to multiple arrests at shopping centres, while, in another case, suspects fleeing a Tshwane Metropolitan Police roadblock attempted to scale an estate wall. Stallion’s monitoring flagged the breach, armed response intervened, and police used Stallion’s intelligence to track and arrest the remaining suspects – a clear demonstration of private innovation supporting national security efforts.

Looking ahead, Stallion plans to expand its AI capabilities into health and safety monitoring, detecting falls, flagging non-compliance with protective equipment, and managing overcrowding. Furthermore, fully integrated site and building management solutions are also in development. All systems are underpinned by strict data protocols, with sites connecting via VPN to secure control rooms and personal data safely retained within Stallion’s environment.

“AI is redefining how security is delivered. By combining technology with trained personnel, we’re not only responding faster but preventing crime and creating safer environments for businesses and communities across South Africa,” concluded Willemse.

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Deepfakes began to be widely created in 2017 after they’d first appeared on Reddit, a discussion website of forums where people exchange information. A Reddit user called Deepfakes shared an AI software tool that could superimpose celebrities’ faces on pornographic videos. AI-generated media became widely accessible through software apps that enable people to freely create deepfakes.

There are several types of deepfakes:

text deepfakes in the form of fake receipts and identification documents

photo deepfakes, often swapping faces and bodies using apps to create memes

audio deepfakes, where text-to-speech apps are used for voice cloning, often targeting politicians

video deepfakes, where face and movement are transferred onto someone else’s video, commonly used to create “revenge pornography”.

Deepfakes pose three main dangers:

They deceive audiences into believing fabricated media.

They enable cybercrimes, reputational harm and misrepresentation.

They can be published by anyone, including anonymous social media users.

The key issue is how law can protect people from the illegal use of their images, voices, and likenesses in deepfakes.

Since 2020, I have looked at laws that regulate deepfakes in South Africa and their implementation. My findings show that the biggest problem with deepfakes is law enforcement, rather than any lack of laws that prohibit the unlawful creation and distribution of deepfakes.

Deepfake threats

South Africa has seen notable cases that highlight the growing impact of deepfakes. In 2024, Leanne Manas, an award-winning South African broadcast anchor, was a victim when her image was used in fake endorsement of weight loss products and online trading on Facebook and TikTok.

South African-born businessman Elon Musk also appeared in a deepfake video that induced many South Africans to invest in a financial scam that promised high returns.

In 2025, Professor Salim Abdool Karim, the director of the Centre for the AIDS Programme of Research in South Africa, appeared in a deepfake video showing him making anti-vaccination statements while endorsing counterfeit heart medicine.

Legal protection in South Africa

South Africa has a mixed legal system that combines constitutional rights, legislation and common law rules to provide deepfake victims with remedies.

There are laws that provide remedies in both civil and criminal cases. For example:

Cybercrimes Act 19 of 2020: criminalises electronic publication of intimate private images without consent.

Electoral Act 73 of 1998: bans publishing false information to influence elections.

Films and Publications Act 65 of 1996: prohibits online distribution of private sexual photographs and films to cause harm.

Protection of Personal Information Act: prohibits misuse of personal information that infringes privacy.

Common law remedies

Anyone can claim violation of privacy if their private images are used without permission. They can also enforce their right to identity if a deepfake misrepresents them or gives a perpetrator commercial advantage.

I investigated these principles in an article about the impact of deepfakes on the right to identity in South Africa. Using South African cases, I found that the unauthorised use of a person’s identity attributes in a deepfake deserves protection.

The Supreme Court of Appeal confirmed, in Grütter v Lombard, that South African law protects a person’s identity from being exploited without permission. And this protection is supported by the constitutional guarantee of human dignity. Grütter and Lombard once practised on the same premises under the name “Grütter and Lombard”, but Grütter later left. Lombard kept using Grütter’s name without consent. The court ordered him to stop as it falsely implied an ongoing professional association and infringed Grütter’s right to identity.

In another case, a surfer’s magazine called ZigZag published a photo of a 12-year-old girl as a pin-up cover image. The court stressed that the key issue was whether an image was exploited for another’s benefit without consent. The defendants were ordered to pay compensation and costs.

Another case is that of South African television personality, beauty pageant titleholder, businesswoman and philanthropist Basetsana Kumalo. She sued a business that took photos of her while she was shopping in their store and used those images in an advertisement for their products without her permission. The court ruled that using someone’s likeness for false endorsements infringes identity and privacy, because it creates the misleading impression of support for the product, service or business.

These cases fit squarely into the deepfakes misuses, showing that false endorsement, election disinformation and non-consensual pornography on social media can trigger liability.

Enforcement challenges

While South African law provides remedies against deepfakes, four hurdles frustrate enforcement:

South African courts have capacity constraints and struggle to resolve backlogs.

Litigation remains a “rich man’s” option. The poor struggle to access justice or wait too long for pro bono help.

While South African courts can assert jurisdiction over global platforms like Meta and TikTok, serving court orders abroad and compelling compliance is still costly, and takedown notices are often enforced too late.

Perpetrators hide behind fake profiles and are hard to trace through the South African Police Service. Social media companies delay revealing the perpetrators’ true identities upon request.

These enforcement challenges can be addressed through capacity building and legal reform. AI research centres should work with law enforcement to train personnel and provide practical skills and tools for tracing and authenticating deepfakes. Parliament must update social media laws so that platforms are directly accountable for fast and fair action when people’s identities are misused in deepfakes.

Legal rules should set minimum standards that deepfake apps and platforms must follow. Rather than relying on age restrictions or consent alone, the law should require these tools to embed watermarking to signal that content is a deepfake, enable tracing of where it comes from, and make sure takedown systems actually work.

Justice on paper

South African law clearly prohibits the misuse of identity through deepfakes, but enforcement gaps leave victims exposed. Without affordable legal access, faster platform accountability, and effective international cooperation, illegal deepfakes will continue to increase.

Written by Nomalanga Mashinini, Senior Lecturer, University of the Witwatersrand.

Republished with permission from The Conversation. The original article can be found here.

The post Deepfakes and South African law: remedies on paper, gaps in practice appeared first on ProtectionWeb.

Michelle Bala, Head of Training at MSHEQ Health and Safety Consultants, said “learners now require flexibility, relevance, and interactivity. They require training which is interactive, relevant to their real work, and available at any time and place.”

At the recent Stallion Integrated Technology Day in Johannesburg, she showcased some of the most innovative MSHEQ practices. QCPR (quality cardiopulmonary resuscitation) manikins provide instant CPR feedback, and virtual classrooms enable interaction through polls, breakout rooms, and collaboration.

Most exciting, however, was Virtual Reality (VR). “VR offers safe, immersive spaces where learners can identify hazards, rehearse emergency actions, and learn by doing without posing any risk to the real world,” she said.

Bala emphasised that training is not a box-ticking exercise anymore. It is making employees skillful to address unforeseen challenges.

“Our ambition is straightforward: train learners with the appropriate skills safely and efficiently,” she added. “We are people – no pretension, just compliance.”

In the future, Bala believes that tremendous potential lies in AI-based adaptive learning, which adapts content to the pace and performance of individual learners.

“The future is personalised,” she went on. “If technology can help make every learner competent faster and safer, then we are not just improving compliance – we are saving lives.”

MSHEQ specialises in occupational health, safety, environment, and quality services, training, and food safety consulting. Their services include conducting safety audits, creating site-specific safety files, and ensuring legal compliance for various industries such as mining, construction, civil engineering, retail, hospitality, and agriculture.

A key innovation is the company’s pioneering use of VR training, which provides immersive, interactive, and risk-free simulations to enhance learning outcomes significantly. This technology allows learners to practice emergency responses and hazard identification in realistic settings, improving skill retention and preparedness. The company also integrates AI for adaptive, personalised learning experiences that adjust to individual performance, accelerating competency development and safety.

The post From VR to AI: MSHEQ highlights tech-driven evolution in employee training appeared first on ProtectionWeb.

History Of Deepfakes

The term “deepfake” burst into public consciousness in late 2017 and originated from Reddit, where a user of the same name shared manipulated pornographic videos, often of celebrities, by superimposing their faces onto existing footage. This initial, notorious application combined “deep learning” algorithms with “fake” media, and the release of underlying open-source code made the creation process available to everyone, allowing individuals with moderate technical skills to produce their own versions.

Since then, the technology has evolved at a breakneck pace, moving from these early, often discernible, manipulations to increasingly sophisticated and convincing fakes capable of being used for political disinformation, financial scams, and even live impersonations, marking a swift and concerning progression from niche internet phenomenon to a mainstream societal challenge.

Case Studies: The Real-World Impact of AI Impersonation

High-profile cases have demonstrated the devastating potential of this technology. The following two case studies served as the source of inspiration that drove the research MWR CyberSec did into this subject.

Case Study 1: The Elon Musk Deepfake Scams in South Africa

In South Africa, a series of sophisticated deepfake scams emerged, leveraging the likeness of Elon Musk and other prominent local billionaires like Johann Rupert and Patrice Motsepe. Scammers created convincing videos where these personalities appeared to endorse AI-powered cryptocurrency trading platforms. These deepfakes promised outlandish returns – for instance, turning a R4,700 investment into R30,000 in a single day.

The deepfake videos were remarkably well-produced, with “Musk’s” voice even mimicking local accents to enhance credibility. These videos circulated widely on social media, with some attracting hundreds of thousands of views. The consequence was substantial financial loss for numerous investors. In one documented instance, an individual invested and lost R5 million to one such scheme. While precise overall numbers are hard to ascertain, the widespread nature of these campaigns suggests that over 150 individuals could have fallen victim to this one specific campaign.

The following two videos show how one such deepfake scheme was created using a real existing interview of Elon Musk on Wall Street Journal and lip syncing it to a different audio source.

This case highlights not only the commercial impact through direct financial losses but also the significant social impact, as it erodes public trust and preys on the familiarity and authority of well-known figures.

Case Study 2: Arup’s $25 Million Lesson in Live Deepfake Deception

In a chilling demonstration of how deepfakes can infiltrate corporate settings, a multinational engineering firm called Arup fell victim to a $25 million scam in 2024.

A finance employee based in the company’s Hong Kong office received an email, purportedly from the UK-based Chief Financial Officer (CFO), requesting urgent and confidential fund transfers.

Initially, the employee was sceptical about the email, however, he was then invited to a video conference call. On this call, attackers convincingly impersonating the CFO and other senior executives (whose likenesses and voices were deepfaked in real-time) assured the employee that the instructions were legitimate and allayed concerns. Convinced by what appeared to be a legitimate, multi-participant video meeting with trusted colleagues, the employee authorised transfers amounting to approximately $25 million (HKD 200 million) to accounts controlled by the fraudsters.

This incident was a stark wake-up call, proving that live, real-time deepfakes are now sophisticated enough to deceive professionals in a business environment. The attack resulted in massive financial loss and underscored the potential for severe reputational damage to organisations that fall prey to such schemes. It also highlighted the psychological manipulation involved, as the live video interaction effectively overrode the employee’s initial skepticism.

How These Things Actually Work: The Technology Behind the Deception

Deepfakes leverage sophisticated AI to superimpose existing images and videos onto source images or videos (for video deepfakes) or to synthesise a target person’s voice (for audio deepfakes). Here is a general overview of the process that goes into making a deepfake:

Phase 1: Data Collection

For both video and audio deepfakes, source material is required for the target person that will be deepfaked. Typically, a significant amount of high-quality video, images and audio is required of the target person in order to train AI models to perform the deepfake.

To perform training, processing has to be performed on the source data such as transcribing audio, cropping images, feature extraction, and various other actions that enhance the training process.

Phase 2: Model Training (Teaching the AI)

This is the most computationally intensive phase of the process. The source material gathered by the attacker is fed to a model and training is performed. For video content, this involves learning the unique facial features, expressions, and nuances that make up the target person. For audio training, it involves learning the characteristics of the target’s voice, including pitch, timbre, intonation and rhythm.

Phase 3: Generation & Refinement (Creating the Fake)

Once the model has been trained, a deepfake can be produced. This can take the form of pre-recorded video or a live performance deepfake. The trained model from phase 2 generates the target person’s face or voice based on the input it receives from either a driving video or webcam and microphone.

For pre-recorded content, post processing and refinement could also be performed to make the deepfake look and sound more realistic. Lip syncing could be performed or visual artifacts might be edited or hidden with overlays.

Pre-trained models

Various pre-trained models are also available that can be used to produce deepfake content. These tools completely eliminate the need to perform vast source material gathering or training a model for millions of iterations, but rather allows a user to upload a short audio clip or even a single picture in order to start the deepfake process. The following video from research done by Bytedance shows just how advanced these pre-trained models can be.

It should be noted that the tool mentioned above was not publicly available at the time of writing due to ethical concerns by the developers (Good!). It does however demonstrate that with the rapid progression of AI tools in our modern age, the limitations for creating deepfakes are becoming less and less of a barrier to entry for attackers.

Technical Shortcomings Seen In The Practical Application Of Deepfakes

During MWR’s own attempts at recreating these techniques, some technical shortcomings were encountered that could help identify poorly made deepfakes.

Audio Deepfake Shortcomings

• Unnatural Cadence and Pace: AI models can struggle with rhythm. Listen for speech that is unnaturally fast or slow, as this can cause the AI to generate noticeable glitches or distortions.
• Volume Changes: Rapid volume changes from loud to quiet or vice versa can often lead to audio artifacting (that robotic sounding voice) being produced by the model.
• Whispers: Whispering lacks strong vocal cord vibration (pitch), which is a key feature that audio models rely on. Consequently, cloned whispers often sound distorted, breathy, or may have bizarre tonal inclinations.
• Context is King: The most powerful detection tool is your own familiarity with the person supposedly speaking. If you know them well, you may notice that their diction, tone, or emotional inflection is “off”. Trust your intuition if the voice sounds like them, but the way they are speaking doesn’t.
• Vocal Range Mismatch: Real-time voice changers are particularly vulnerable when there’s a significant difference between the input and target voices. For example, if someone with a naturally high-pitched voice attempts to clone a very deep, low-pitched voice in real-time, the output may sound strained, tinny, or unstable. This doesn’t help that much in detecting these as an attacker would likely pick a target that more closely resembles their own voice.

The audio samples below demonstrates some of the shortcomings:

Video Deepfake Shortcomings

• Masks: A deepfake model needs to constantly detect a face in the source video to overlay a target face onto it. If this detection is interrupted—perhaps by a hand passing in front of the face or gestures that the source video didn’t cover (think sticking your tongue out), or poor lighting, the “mask” can break. The results are often jarring and obvious, ranging from features being incorrectly mapped to the fake face momentarily vanishing altogether.
• Unnaturally Smooth Skin: The deepfake generation process often involves compressing and then reconstructing facial features. This can lead to a loss of fine detail. Look for skin that appears unnaturally smooth, almost like a digital airbrush has been applied. Details like pores, wrinkles, fine hairs, or even stubble may be smoothed over or absent entirely, giving the person a doll-like appearance.
• Irregular Gestures and Behaviour: This is another context-based clue. We all have unique mannerisms, head tilts, and hand gestures that accompany our speech. A deepfake may replicate a face perfectly, but if the gestures or expressions don’t match the person you know, it’s a major red flag. If a normally animated friend is suddenly stiff and inexpressive on a video call, or vice versa, it could indicate that you’re watching a digital puppet, not a real person.

The video below demonstrates some of these shortcomings in an exaggerated manner:

Mitigations and Staying Vigilant: What Can Be Done?

The U.S. Department of Homeland Security (DHS), in its report “Increasing Threats of Deepfake Identities” emphasises that the threat of deepfakes comes not just from the technology itself, but from our natural inclination to believe what we see and hear. Even less sophisticated deepfakes can be effective in spreading misinformation.

The DHS report outlines that there is no single, universal solution to the deepfake problem. Instead, a multi-pronged approach is necessary, encompassing the following phases of a deepfake attack:

1. Technological Innovation:

• Developing and improving deepfake detection technologies. This is an ongoing “cat and mouse” game as generation techniques become more advanced.
• Exploring digital watermarking or authentication technologies that can help verify the authenticity of media.
• This phase is largely dependent on developers and organisations that have to consider the ethical implications of what they are developing but also how these safety measures could be added.

2. Education and Awareness:

• Critical Evaluation of Media: Individuals need to be educated to critically evaluate online content, especially if it seems sensational or too good to be true. Look for inconsistencies in media, including unnatural features visible artifacting and what some call “uncanny valley”.
• Source Verification: Always try to verify the source of information. Is it from a reputable news outlet or official channel? Be wary of content shared widely on social media without clear attribution.
• Awareness of Impersonation Tactics: Understand that AI can be used to impersonate executives, colleagues, or public figures. For sensitive requests, especially those involving financial transactions or confidential information, use out-of-band verification (e.g. a phone call to a known number, or an in-person check if possible) before acting.

3. Regulation and Policy:

• Developing legal frameworks and regulations to address the malicious use of deepfakes, including issues of consent, fraud, and defamation.

4. Public-Private Cooperation:

• Encouraging collaboration between government agencies, research institutions, and private sector companies (including social media platforms and tech developers) to share information, develop standards, and implement safeguards.

Individual Precautions:

The DHS highlights different phases and threat actors, however, the core advice for individuals to protect themselves includes:

• Be Skeptical: Approach unsolicited communications or unusual requests with caution, even if they appear to come from a known person.
• Verify Identity: If you receive a suspicious video call or audio message, try to verify the person’s identity through a different communication channel that you know is legitimate. Ask questions that only the real person would know.
• Look for Tell-Tale Signs: While deepfakes are getting better, some artifacts may still be present:
  • Unnatural eye movements or lack of blinking.
  • Awkward facial expressions or lip-syncing.
  • Blurring or distortion, especially where the face meets the hair or neck.
  • Strange lighting or skin tones.
  • Audio that sounds robotic, has an unusual cadence, or lacks emotional depth.
• Report Suspected Deepfakes: If you encounter a malicious deepfake, report it to the platform where you saw it and, if appropriate, to law enforcement.

AI-driven impersonation is a rapidly evolving threat that poses significant risks across personal, commercial, and societal domains. As the technology becomes more accessible and sophisticated, the potential for misuse grows. By understanding how these deepfakes are created, learning from real-world incidents, and adopting robust mitigation strategies that combine technological solutions with critical human awareness, we can better defend ourselves against this new wave of digital deception. Staying informed and vigilant is our first and most crucial line of defence.

The post The alarming rise of AI impersonation: When seeing and hearing isn’t believing appeared first on ProtectionWeb.

According to a recent Kaspersky study, the number of cyberattacks experienced by organisations in the last 12-months is reported to have increased by 29% in South Africa. The most ubiquitous threat came from phishing attacks, with 67% of those questioned in South Africa reporting this type of incident. With AI becoming a more prevalent enabler for cybercriminals, over half of the respondents in South Africa (53%) anticipate significant growth in the number of phishing attacks. In this text, Kaspersky examine how AI is used in phishing and why experience alone is sometimes not enough to avoid becoming a victim.

Personalisation through AI

Previously, phishing attacks relied on a generic mass message sent to thousands, hoping some of the recipients would fall for the bait. AI has changed this into scripting highly personalised phishing emails in large numbers. Using publicly available information like that on social media, job boards, and companies’ websites, these AI-powered tools can generate emails tailored to an individual’s role, interests, and communication style. For example, a CFO might receive a fraudulent email that mirrors the tone and formatting of their CEO’s messages, including accurate references to recent company events. This level of customisation makes it exceptionally challenging for employees to distinguish between legitimate and malicious communications. 

Deepfake technology

AI has also introduced deepfakes into the phishing arsenal. These are increasingly being leveraged by cybercriminals to create fake but highly accurate audio and video messages, crafted to reflect the voice and appearance of the executives they seek to impersonate. For example, in one reported case, attackers used a deepfake to impersonate multiple members of staff during a video conference, convincing the employee to transfer approximately $25.6 million. As deepfake technology continues to advance, it is expected that such attacks will become more frequent and harder to detect. 

Bypassing traditional defenses

Cybercriminals can manipulate the script of traditional e-mail filtering systems with the use of AI. By analysing and mimicking legitimate email patterns, AI-generated phishing emails can bypass security software detection. Machine learning algorithms can test and refine phishing campaigns in real time, enhancing their success rates and making them increasingly sophisticated.

Why experience is not enough

Even experienced employees are falling victim to these advanced phishing attacks. The level of realism and personalisation that AI can achieve may override the skepticism that keeps experienced professionals cautious. Moreover, AI-generated attacks often exploit human psychology, such as urgency, fear, or authority, pressuring employees into acting without double-checking the authenticity of the request.

Combatting AI-hyped phishing

To defend against AI-driven phishing attacks, organisations must adopt a proactive and multi-layered approach that emphasises comprehensive cybersecurity. Regular, up-to-date AI-focused cybersecurity awareness training is critical for employees, helping them identify the subtle signs of phishing and other malicious tactics. Kaspersky Automated Security Awareness Platform can help with such training. Alongside this, businesses should implement robust security tools, such as Kaspersky Next and Kaspersky Security for Mail Server, capable of detecting anomalies in emails, such as unusual writing patterns or suspicious metadata.K

A zero-trust security model also plays a vital role in minimising the potential damage of a successful attack. By restricting access to sensitive data and systems, this approach ensures that even if attackers breach one layer of security, they cannot compromise the entire network. Together, these measures create a comprehensive defense strategy, combining advanced technology with vigilant human oversight.

The post The evolution of AI in phishing attacks: Why even the most experienced can fall victim appeared first on ProtectionWeb.

University of Pretoria electronic engineering student Neil-John Lord, speaking at the recent SA Radar Interest Group conference at the Council for Scientific and Industrial Research, discussed the problem of wildlife poaching and described previous efforts at identifying poachers. These included the use of camera traps in the Pilanesberg game reserve and the use of patrols and other methods.

These methods were not satisfactory due to false alarms with the camera traps: wind moving trees, leaves falling in front of the sensors or the movement of animals. Of the useable 10 percent of data, only a small subset were poachers and not animals.

He said most motion-detection models were developed in urban environments, which did not translate well to the bush and the difficulty of operating at night exacerbated the problem.

A University of Pretoria team then augmented the camera traps with additional sensors including lidar and radar sensors to detect motion (such as that of poachers) at night. The added sensors could give the observers distance to target, velocity and other extended information (size, orientation and shape) of the target.

Lord explained that with earlier radars, a target was picked up as a single return. With the advent of millimetre band radar, numerous returns were received from the same target. The target points on the radar screen were further clarified by use of an algorithm to create a ‘point cloud’. This can be averaged to get a very accurate measurement of where the target is at a given time. But a lot more can be done.

Target tracking and target classification are critical problems in battlefield surveillance systems, Lord explained. Traditionally, military radars detected and tracked targets, but classifying them is a new development in the digital age.

In World War II, for example, radars could detect enemy aircraft, but could not tell the number or type, only height and vector (size and direction). Modern systems, using improved radar and digitised software, including artificial intelligence (AI), can separate out the height, vector and often, the general type of aircraft, such as a fighter, passenger aircraft, cargo plane, or a missile. The software’s ability to ‘classify’ radar targets can make all the difference to success in the battlespace.

Getting back to the poaching problem, Lord explained that the difficulty was that, unlike ships or aircraft, sizes of animals and poachers were unknown. He said that sizes varied drastically, from elephants to impalas, but both are animals. He pointed out that separate classes could be created for separate species, but ‘that could get complicated quite quickly’.

Lord and his team discovered that the main difference between humans and animals was that humans exhibited bipedal motion, giving a different radar return to animals with quadrupedal motion. Once this difference was fed into the radar computer, it could create two ‘classes’, one for humans, one for animals. Two possible classifications were created, one based on orientation, and one on size.

Lord said he hoped the findings could be incorporated into existing poacher detection and tracking systems.

Drone detection

William Bourn, representing the University of Cape Town, told conference delegates of the promising use of millimetre band radar, specifically Frequency Modulated Continuous Wave (FMCW) radar, to detect drones and small targets.

With the increased use of drones used by terrorists, armed forces and civilian troublemakers, such as those who disrupted air traffic at Gatwick Airport, London in 2018, drone detection has become a priority, he explained.

He described existing challenges as ‘teaching’ software with machine learning to distinguish between small objects, such as small drones and birds. While much work needs to be done, the study of millimetre band radar promises solutions for a host of tasks such as assisting civil aviation at airports, catching poachers or preventing terrorists using drones to reconnoitre or attack military or civilian targets.

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Leso Legadima, Media Liaison Officer for Department of Cooperative Governance, Traditional Affairs, and Human Settlements, said the adoption of modern technology is essential in fire safety.

“Investing in next-generation fire-fighting solution is not just about being prepared; it’s about being committed to the protection of life, property, and critical infrastructure. Our focus is to utilize the best tools at our disposal to ensure that our emergency services are future-ready.”

Legadima emphasised that artificial intelligence has been the core of a very new approach to fire detection and response.

“AI algorithms can analyse data from sensors, weather forecasts, and historical fire incidents to predict the possibility of a fire outbreak. That predictive capability will help authorities better allocate their resources and reduce risks before they become fully fledged disasters. AI-powered drones fitted with thermal imaging cameras present first responders with real-time aerial visuals of fire-prone areas. These drones can navigate through thick smoke and rough terrain, offering a bird’s-eye view of the situation to firefighters on the ground,” he added.

He underlined that along with drones and AI, block-chain technology has been researched to secure highly sensitive data regarding fire safety. “The immutable ledger in block-chain will be useful in storing and sharing incident reports, records of inspections, and resource deployment with transparency and accountability in any fire department. It will also pave the way for smooth coordination among all concerned departments like municipal authorities, rescue services, and insurance providers,” he added, noting that fire-fighter training is also becoming very futuristic with the incorporation of AR-based simulations.

“Wearing AR headsets, trainees are exposed to very realistic fire scenarios and learn how to make their way through smoke-filled rooms in search of victims and extinguish flames. This better prepares them for real-life situations and makes them more efficient and competent.”

Smart fire stations are one of the forthcoming innovations, Legadima further revealed.

“The technologically advanced hubs possess intelligent resource management systems where equipment, personnel, and vehicles availability is monitored. Smart fire stations will deploy IoT devices to track health and readiness of fire engines on whether they are ready to respond at any

given moment, hence the real-time monitoring reduces response times by large margins and also ensures that the firemen are well prepared to respond to any emergency.”

The path that will drive South Africa to future-ready firefighting solutions is underpinned by the realisation that traditional methods are not adequate anymore. With climate change making wildfires more frequent and intense, the need for more advanced, data-driven approaches has become increasingly urgent. The country is using innovative technologies in an effort to reduce response times but also to prevent fires in the first place.

Dr. Cosmas Luckyson Zavazava, Director of the International Telecommunication Union’s Telecommunication Development Bureau, underlines the role that technology plays in modern fire management. “ITU is technology neutral. With the evolution of technology comes a new opportunity to save lives. Drones can reach dangerous places or make it possible to monitor the progression of disasters such as fires,” he explained.

He further emphasized that the importance of telecommunications infrastructure provides connectivity with sensors and IoT; therefore, it is needed and indispensable. Dr. Zavazava added emphasis to 5G technology, “5G and IoT are important for disaster preparedness, particularly 5G which has low latency and is ideal for Artificial Intelligence. These technologies are also ideal for early warning and timely disaster response.” He highlighted the need for regulatory frameworks, adding, from our perspective, applicable regulations on the deployment and use of Information and Communication Technologies apply even in tech for fire management. What should stand out are Standard Operating Procedures (SOPs).”

The ITU plays a leading role in enabling these advancements.

“ITU is at the forefront, providing universal connectivity, enhancing capacity building in order to develop skills, policy and regulations, and designing the National Emergency Telecommunication Plans,” noted Dr. Zavazava. “Also, we provide Standard Operating Procedures development support. And finally, ensuring that this infrastructure is resilient, reliable and robust is key to assure connectivity is not disrupted”.

His vision again reassured the ITU’s commitment to using telecommunication for disaster preparedness and emergency response in ways that will help further South Africa’s journey to future-ready firefighting solutions.

Equally, South Africa is going a notch higher in changing this narrative of fire safety through its Disaster Management Centre (DMC). According to Ms Zukiswa Poto of the University of the Free State’s Disaster Management Training and Education Centre for Africa, DiMTEC, the DMC is adopting drones, data analytics, and AI technologies to enhance fire detection and response systems. For example, AI algorithms are used to analyse historical fire data to predict where outbreaks are likely to occur, thus enabling proactive measures. Real-time monitoring by drones with thermal imagery enhances the understanding of fire events by disaster managers and firefighters, thus improving situational awareness and resource allocation.

Besides this, the DMC cooperates with academic institutions and research organisations to drive fire safety innovation. Research projects range from the development of new materials for firefighting equipment to prevention measures and even to novel methods of fire suppression.

Such efforts go a long way in extending the horizon of fire safety technology in South Africa and make firefighting operations more effective and efficient.

New technology is also transforming the way firefighters are trained. The DMC has adopted AR and VR for training purposes. AR simulations allow firefighters to practice their skills in realistic scenarios without the risks associated with live training. This immersive method enhances learning outcomes and prepares the trainees for the challenges they may face in real-world fire situations. Similarly, VR training modules provide practical experience in controlled environments that help build confidence and decision-making under pressure.

Nana Radebe-Kgiba, Spokesperson for City of Johannesburg Emergency Services, has confirmed that while drones are not in use by Johannesburg’s EMS, the department is actively working on training personnel for drone certification.

“EMS has plans to adjust to the usage of smart technology in fire stations. This will include the incorporation of a mobile pad used by all units in storing building plans and inspections done. This will give the responding firefighters more information in regard to a building on fire even before arrival. Budgetary constraints and supply chain challenges are deterrents to the implementation of new technologies, but the City of Johannesburg is determined to continue training its personnel through accredited service providers in the proper deployment of such tools,” she said.

The United Nations Office for Disaster Risk Reduction (UNDRR), represented by Associate Communications Officer Justine Dumas, recognised the transformative potential of new technologies in disaster risk reduction (DRR) efforts. The organization has been very effective in the development and application of technologies like machine learning and artificial intelligence. In collaboration with Google and WFP, the SKAI tool provided critical risk mapping during the 2022 Durban floods. This technology helped analyse infrastructure damage and supported decisions for rescue teams. This tool’s success underlines the role of AI in enhancing DRR and saving lives.

UNDRR is also closely involved with on-going South African work regarding EWS, an integral part of disaster preparedness. Being a leader in the SADC region, it is envisioned that South Africa will contribute to regional collaboration and sharing best practices for DRR, especially towards emerging wildfire risks and extreme heat scenarios.

Jaco Keet, a technical sales engineer with Cobra Projects, which manufactures and supplies fire and rescue vehicles, shed light on the challenges faced by South Africa’s fire brigades.

“In general, the fire brigade service throughout SA, with a few exceptions, is not in a good place. They are under-equipped and under-staffed, with budgets that cannot address their requirements. They need to focus on acquiring basic equipment before they can invest in new technologies like drones, AI, and AR.” While he acknowledges the potential benefits of these technologies, Keet emphasised the need to first address the backlog in fire service readiness.

Another tool gaining traction in this sphere of fire safety management is Geographic Information Systems (or GIS) technology. According to Lauren Sweidan, Marketing Manager at Esri South Africa, GIS provides an important function in that it enables more effective fire safety planning

and response. Esri SA tools are created to support the generation of fire risk maps, through the identification of areas vulnerable to both urban and wildfires based on vegetation, topography, and human activities. She further said that GIS is also used in integrating remote sensing data from satellites and drones, enhancing the accuracy of these maps, hence helping authorities’ better plan mitigation and response strategies.

Another agency that has embraced technology in its fire management operations is the South African National Parks. JP Louw, Head of Communication and Spokesperson for SANParks, noted that the implementation of the Incident Command System (ICS) has enabled better coordination among various agencies involved in wildfire management. The use of drones in SANParks has revolutionised fire management, particularly in remote areas. Drones provide invaluable data before, during, and after fires, from monitoring vegetation moisture content to pinpointing hotspots and assessing post-fire recovery. SANParks also leverages real-time data analytics to improve fire predictions and resource deployment.

Meanwhile, there is a standout innovation spearheaded by Lumkani – a South African company focused on fire detection and prevention in informal settlements. According to their website, they have implemented one of the most impactful innovations; Lumkani’s Internet of Things-enabled heat sensors which detect rapid temperature changes, enabling early fire detection. The system is integrated into a community-wide alert network, ensuring that fires are contained before they spread. With over 40,000 homes equipped with these devices, Lumkani’s work has been instrumental in reducing fire spread by as much as 71%. Additionally, Lumkani offers micro-insurance coverage in collaboration with Hollard Insurance, providing both fire detection and financial protection for vulnerable communities. This model has gained global recognition and has been awarded for its innovative approach to emergency response.

These contributions reflect South Africa’s commitment to modernising its fire safety strategies through technology. The country is building a resilient firefighting system, ready to protect lives, properties, and natural resources amidst the growing threat of fires, through integrating emerging tools and leveraging international collaborations. These divergent efforts at the government, academia, private industry, and international organization levels are testament to the collaborative drive toward a future-ready firefighting ecosystem.

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This was detailed by Sparcx Managing Director Sujo Mulamattathil, who was speaking at the Aerospace Industry Support Initiative (AISI) Industry Day hosted by the Council for Scientific and Industrial Research (CSIR) on 29 November.

The RTC system is currently being developed for a defence customer, although it could also be used in public safety. Mulamattathil said RTC systems often struggle to accurately classify targets due to several challenges such as clutter, noise, multiple targets and rapidly changing environments. This lack of precision impacts safety and accuracy.

Consequently, Sparcx is building an AI-based system to identify targets faster and more accurately, and is making use of expertise at the CSIR and Reutech Radar Systems to do this – funding for the project is being provided by Sparcx, the AISI/Department of Trade, Industry and Competition, and Reutech Radar Systems.

Mulamattathil said the next phase of development is size, weight and power optimisation, industrialisation, manufacturing, marketing and sales. The company’s business model is to sell its RTC system hardware with radars manufactured by companies like Reutech.

He added that a combination of radar and camera technologies to identify and better classify targets is being integrated. Use cases include farm security, wildlife monitoring, border security etc. as the system is able to tell the difference between a vehicle and, say, an animal or a human being.

Sparcx is a wholly black military veteran owned and managed electronic engineering company focussing on the aerospace, defence, and public safety sectors. It is developing several technologies, some in collaboration with the CSIR, to serve the African market.

For example, Mulamattathil explained that this year Sparcx has been developing an artificial intelligence (AI)-based automatic speech recognition system for an aerospace customer to reduce incursions/accidents at airports. Other projects include implementing less lethal devices for a law enforcement agency, and industrialising AI-based smart water network sensors to reduce leakage losses for municipalities and water boards (up to 60% of municipal water is lost countrywide due to leaks).

In 2024 Sparcx was chosen as the sole South African representative in a cohort of 10 businesses from Africa in Qualcomm’s “Make in Africa 2024” start-up incubation programme. It was selected on the basis of its RTC system.

Since its inception in 2015, Sparcx has developed a number of different technologies, such as a radio frequency measurement system for the government, and an AI-driven runway occupancy alerting system.

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Artificial intelligence (AI) can alleviate this burden. By processing massive amounts of data quickly, it can map the movements of offenders and illicit goods, identify patterns in criminal behaviour and activities, and make focused connections.

AI has been recognised globally for its potential to save police hours of search and analysis work. In April, Britain’s government outlined how £230 million would be spent on AI technology to help the police save 38 million hours of police time. The European Union is implementing a project that uses AI to provide comprehensive intelligence to detect organised crime.

Policing organised environmental crime in particular is expensive, laborious and complex. These crimes often occur in remote, hard-to-access areas, involve different networks of actors, and cut across jurisdictions. Despite their significant costs to the environment, economy and society, environmental crimes tend to be a lower priority for law enforcement.

Research by the ENACT organised crime project at the Institute for Security Studies (ISS) shows how AI can do some of the resource-heavy and complex aspects of investigating environmental crime in Africa.

TrailGuard AI is a system of cameras that enables national park officials to detect, stop and arrest poachers before they kill wildlife. The tiny cameras are easily camouflaged, and placed along trails where local intelligence has identified a threat. AI models filter out 99% of false positive images, saving battery life in remote places.

Eric Dinerstein, Director of Nightjar at the non-profit organisation RESOLVE, which helped develop TrailGuard, told ISS Today that with good cell transmission, an image triggered by wildlife or poachers can reach a cellphone within around 30 seconds. This enables the appropriate authorities (e.g. park rangers or police) to mount a real-time response. The system also works with other anti-poaching interventions, such as sniffer dogs.

TrailGuard technology was first deployed at Tanzania’s Singita Grumeti Reserve in 2018. It enabled the arrest of 30 poachers and the seizure of almost 600 kg of illegal bushmeat during a test phase in East Africa.

Operation Pangolin was launched in 2023 as a collaboration between universities, conservation initiatives and Gabon’s National Agency for National Parks. It collects and processes data from existing trail cameras, using AI to recognise pangolins from camera traps and thermal cameras. The imagery is used with Spatial Monitoring and Reporting Tool data from ranger patrols to build predictive models for pangolin poaching. The project’s long-term aim is to develop separate AI models that help predict trafficking routes and markets.

The project currently operates in Gabon and Cameroon and works closely with Nigerian stakeholders. Its team is exploring ways to build local capacity so that the data, technologies, and tools continue to be used and offer value beyond the project scope. Team member Bistra Dilkina told ISS Today that the ‘AI tool is an empty shell without local data. We need local champions embedded in the project.’

Skylight is a marine data platform that applies AI-powered pattern recognition, computer vision and machine learning to satellite data. Skylight uses ship movement identification and analysis from subject matter experts and rapidly applies it globally to detect illegal fishing across the oceans.

It alerts coastguards and other maritime enforcement agencies to suspicious vessel patterns and locations, allowing them to assess potential non-compliant or illegal activity and distinguish it from ‘normal’ behaviour. Madagascar, Kenya, Gabon, and nations around the Gulf of Guinea are among the 70 countries that use the platform. Officials use their knowledge of national laws and their institutions’ priorities, mandates and resources, to determine how to respond.

The data is received quickly enough that law enforcement can act fast, enabling timely intervention if necessary. According to Ted Schmitt, Senior Director of Conservation and Programme Manager for Skylight, coastguards had used their data to board vessels and uncover illegal fishing activity.

Digital Earth Africa (DEA) takes vast raw geospatial satellite data from across Africa and translates it into analysis-ready information. Observing changes in land use over time from satellite imagery provides insights into illegal mining activities. For instance, surface-level activities such as creating artificial ponds, clearing vegetation and building access roads may indicate unlawful mining.

Localised, real-time data on illicit mining can help make the deployment of limited resources more cost-efficient and effective. Ghana’s government has partnered with DEA to identify the location of illegal mining activities outside of mining concessions.

There are also challenges and risks to harnessing AI for law enforcement in Africa. These include limitations in the availability and volume of local data and inadequate basic communication and digital infrastructure. A lack of technical skills and resources to respond to environmental crime even when identified, is also a problem, as is limited investment in research and development. There are also concerns about reactive regulatory systems and data privacy, unauthorised surveillance of civilians, and criminal threats.

But AI is here to stay and is advancing quickly. By engaging with AI’s potential, policymakers across Africa could make a real difference in the fight against organised and complex crimes.

This requires a dedicated investment in building the capacity to gather large, local and relevant data sets. Budgets will also need to be allocated to digital and communication infrastructure, and generating the human capacity and skills for AI development and implementation.

With the African Union’s AI White Paper and Roadmap as a guide, African countries should draft and enact legislation on AI to ensure its use is regulated. Meanwhile, public-private partnerships can be leveraged to implement existing, proven AI interventions that can generate powerful crime-fighting tools.

Written by Romi Sigsworth, Research Consultant, ENACT, ISS.

Republished with permission from ISS Africa. The original article can be found here.

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Monitoring the video feed, which is the richest source of data, is expensive and hard to scale. This is according to Natalie Doran, chief executive of Singapore based Lytehouse, who spoke at a Securex 2024 seminar this week.

Outlining the evolution of video surveillance, she referred to early analogue systems, followed by the digitising of the video signal to off-site monitoring and control through live feeds.

With the advent of artificial intelligence (AI), the incidence of false alarms is diminishing, as the systems are getting better at object and face recognition.

“But we are at an early stage where operators still are drowning in alerts, resulting in long response times,” she said. “And the system remains at a level where it is unscalable.”

The solution does not lie in simply increasing services, either in terms of cameras or operators. “How many cameras can an operator truly manage? There is a ceiling to what can be done,” she stated.

Rather, the answer is in automation with AI, which is writing the next generation technology, which is essentially an auto-operator. The AI monitoring the video system can ‘see’ and interpret an incident, and instantaneously ‘write’ a message, or even generate a voice alert.

“Of course, this new technology still involves people, as it requires a human intervention of some sort, but AI takes care of a significant part of the surveillance and monitoring function. And it is certainly scalable,” she said.

The bespoke technology that Lytehouse is offering covers business spheres from mining, retail, warehousing, logistics to real-estate and education.

“In this regard, the risks being addressed are in the areas of health and safety, infrastructural, operational and security,” she concluded.

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