GPT-5.4-Cyber is a version of OpenAI’s latest model adjusted for defensive security work and will not be widely available at launch. Instead, OpenAI is giving early access to selected security firms, organisations and researchers.

The release follows Anthropic’s recent launch of its own frontier model, Mythos. That system is being tested under a restricted programme known as Project Glasswing, where only approved groups can use it for cybersecurity tasks.

According to Anthropic, the model has already identified thousands of serious weaknesses across software systems.

OpenAI is taking a comparable route but with a wider rollout plan over time. The company is expanding its Trusted Access for Cyber programme, which it introduced earlier this year. This scheme verifies users before granting them access to more capable tools.

Under the updated structure, more individuals and teams will be admitted, but access depends on how much information they provide to confirm their identity and role. Those in the highest tier will be allowed to use GPT-5.4-Cyber.

The company said the model has fewer restrictions when handling sensitive tasks such as vulnerability research and code analysis. It is designed to support security professionals who need to examine software more deeply, including analysing compiled programmes without access to their source code.

At the same time, OpenAI is carefully monitoring how the system is used. Because the model allows more freedom, the company is limiting its release and adjusting safeguards as it learns from real-world use.

Tools like GPT-5.4-Cyber can be used for both defence and attack, OpenAI acknowledged that risk, noting that threat actors are already experimenting with artificial intelligence to find new ways into systems.

To manage that, the company said access will not just depend on the model itself, but on who is using it and for what purpose. Strong identity checks and clearer signals of intent are being built into the process.

The aim is to make security tools more widely available without opening the door to misuse. OpenAI said it does not want to decide centrally who gets to defend systems, but it still needs controls that can scale.

This latest release builds on earlier initiatives, including its cybersecurity grant programme and tools designed to scan and fix software vulnerabilities. The company said these systems have already helped address thousands of high-risk issues.

OpenAI expects both risks and benefits to grow, saying future models will likely require stronger protection, even as they provide more advanced support for those working to protect digital infrastructure.

The company said net income for the year reached $1.06 billion, up from $845.7 million in 2024. Earnings per share also climbed, with GAAP EPS rising 29% to $9.62.

In the fourth quarter alone, revenue came in at $745 million, a 6% increase year on year. Subscription revenue stood out, rising 11% to $325 million.

Check Point: Eight Key Trends Will Define Africa’s Cyber Security in 2026

Growth is not coming from one-off sales, it is being driven by recurring security subscriptions, which now account for a large share of total income.

The company also reported calculated billings of $2.9 billion for the full year, up 9%. Remaining performance obligations, which show future contracted revenue, reached $2.73 billion.

Chief Executive Officer Nadav Zafrir said: “We delivered solid fourth quarter and full year 2025 results, with revenue landing above the midpoint of our outlook and EPS exceeding expectations. Our performance remained resilient throughout the year, driven by continued customer adoption across our Hybrid Mesh Network and Workspace platforms.”

He added: “In 2026, our strategy is centred on securing our customers’ AI transformation across the enterprise. We are focused on executing against our four strategic pillars, Hybrid Mesh, Workspace, and Exposure Management, while embedding AI-driven security throughout our portfolio.

“Today’s announced acquisition of Cyata further expands our AI security stack, enabling full discovery, governance, and control of AI agents as organisations accelerate their AI journeys.”

Beyond earnings, the company moved to strengthen its product offering. It announced three acquisitions in early 2026, covering AI security, asset monitoring, and managed service platforms.

Cash reserves more than doubled during the year. Cash, marketable securities and short-term deposits rose to $4.34 billion from $2.78 billion. The increase followed proceeds from a $2 billion convertible notes offering.

At the same time, Check Point returned money to shareholders. It repurchased about 6.8 million shares in 2025 at a total cost of $1.4 billion.

Cash flow from operations also improved, reaching $1.23 billion for the year despite a one-off tax payment linked to prior years.

Check Point is growing steadily, with stronger revenue in 2025, thriving subscriptions, and a larger cash position in 2026.

The new platform, developed in collaboration with NVIDIA, focuses on securing AI models, workloads, and agentic applications used in enterprise environments, without compromising performance.

The company confirmed that AI Cloud Protect is now available for on-premises enterprise use and has been validated on NVIDIA RTX PRO Servers. 

Built on NVIDIA’s BlueField technology, it enables organisations to secure AI model development and inference workloads both in data centres and cloud environments.

“As enterprises race to build AI-driven innovation, they can’t afford blind spots,” said Nataly Kremer, Chief Product Officer at Check Point. “With NVIDIA, we’re making AI factories secure by design—protecting models, data, and infrastructure without slowing innovation.”

The rise of AI has exposed enterprises to new and complex risks. According to Check Point data, one in every 80 generative AI prompts reveals sensitive information, while Gartner reports that nearly one-third of organisations suffered an AI-related security incident in the past year, ranging from prompt manipulation to infrastructure attacks.

AI Cloud Protect was built to address these vulnerabilities head-on. Running on NVIDIA BlueField-3 DPUs, it delivers full-stack protection without consuming CPU or GPU resources, putting an end to the common trade-off between security and performance. 

Its network-level defence prevents unauthorised access, data poisoning, and model exfiltration. At the host level, it leverages NVIDIA’s DOCA Argus framework for direct memory access, offering full visibility into active processes on AI nodes to detect and block malicious workloads, even within downloaded large language models.

“Security is essential for the next generation of AI infrastructure,” said David Reber, chief security officer at NVIDIA. “NVIDIA is working with Check Point to integrate BlueField acceleration and the NVIDIA DOCA Argus runtime security framework into the AI Cloud Protect platform to help enterprises deploy AI confidently.”

The solution will also extend to NVIDIA’s upcoming BlueField-4 DPU, which promises six times more compute power and double the network throughput, setting the stage for faster and more scalable AI operations.

Beyond AI Cloud Protect, Check Point’s AI security portfolio includes CloudGuard Web Application Firewall (WAF) with Lakera integration, designed to block prompt injection and jailbreak threats in enterprise AI applications. 

Another solution, Infinity GenAI Protect, helps organisations monitor generative AI usage, apply policies, and prevent sensitive data exposure across teams. Together, these solutions aim to deliver end-to-end protection across the AI ecosystem, from infrastructure to user level.

The company is currently piloting AI Cloud Protect with select financial services firms and partners like World Wide Technology (WWT), focusing on protecting data centres supporting large language model development.

“As enterprises build AI server factories at scale, the combination of Check Point’s AI Cloud Protect and NVIDIA BlueField acceleration delivers enterprise-grade protection for sensitive AI workloads from model training to inference without compromising the performance modern AI applications demand,” said Chris Konrad, vice president, Global Cyber, WWT.

Check Point is addressing data leakage, model manipulation, and infrastructure threats, while enabling organisations to innovate confidently in this phase of intelligent computing.

This is another step in Europe’s investigation of Chinese tech firms accused of flouting privacy rules.

The Berlin Data Protection Commissioner, Meike Kamp, invoked Article 16 of the EU’s Digital Services Act (DSA) in a formal notice to both tech giants, declaring DeepSeek’s app as illegal content under European law. 

At the heart of the matter is the company’s transfer of user data to China, without any of the legal safeguards mandated by the EU’s General Data Protection Regulation (GDPR).

Kamp stated, “DeepSeek has not been able to provide my agency with convincing evidence that German users’ data is protected in China to a level equivalent to that in the European Union.”

That alone would be enough to raise red flags. But the issue runs deeper.

Germany’s investigation found that DeepSeek violated Article 46 of the GDPR, which governs international data transfers. China does not have an EU adequacy decision, a prerequisite for transferring personal data outside Europe without further protections. 

Yet DeepSeek allegedly failed to implement even basic legal mechanisms, such as Standard Contractual Clauses (SCCs), that could have made such transfers lawful.

According to DeepSeek’s own privacy policy, the app stores an alarming range of personal data, including search queries, chat histories, uploaded documents, and location data, on servers based in China. Nowhere in the policy is GDPR mentioned. No safeguards are outlined. No clarity is given.

This isn’t the first time the company has faced European resistance. Italy’s data protection authority banned DeepSeek earlier this year after it failed to explain how it collects and processes user data. The Netherlands followed shortly after, warning the public not to submit sensitive information through the app.

In the United States, lawmakers are drafting legislation that would prohibit federal agencies from using AI models developed in China. A senior U.S. State Department official told Reuters that “DeepSeek is actively supporting China’s military and intelligence operations, including providing services to PLA research institutions.” 

The same report revealed that the company allegedly used shell companies in Southeast Asia to bypass U.S. export controls and acquire Nvidia H100 chips, restricted hardware used for training advanced AI models.

These concerns have been amplified by DeepSeek’s rapid ascent. The company claimed in January that its AI models, such as DeepSeek-R1 and V3, rival those of OpenAI and Meta—at a fraction of the cost. It reportedly trained its large language model for just $5.6 million, a figure many experts find highly questionable. 

Nevertheless, the apps have surged in popularity, topping download charts across multiple countries, and exposing users’ data to foreign jurisdictions.

Despite repeated requests from German authorities since May, DeepSeek refused to adjust its data practices or withdraw voluntarily from the app stores. With that deadline now past, the commissioner’s office has moved decisively.

Apple and Google are expected to act quickly, but neither company has responded to requests for comment. 

As adoption spreads from the bottom-up, companies are losing control over how sensitive information is being handled, what models are being used, and who has access to what.

Unbound Security AI has raised $4 million to fix this. The oversubscribed seed round was led by Race Capital, with participation from Wayfinder Ventures, Y Combinator, Massive Tech Ventures and others including notable angel investors.

Unbound gives IT teams the visibility and controls they need to safely introduce and manage AI tools in the enterprise. Its AI Gateway plugs into commonly used tools, like Cursor, Roo, Cline or internal document copilots, and provides real-time protection, model routing, and usage analytics. 

From blocking sensitive information leakage to managing model costs and performance, Unbound helps organisations roll out AI on their own terms.

The founding team brings deep experience in both enterprise security and infrastructure. CEO and co-founder Rajaram Srinivasan previously led data security products at Palo Alto Networks and Imperva, and earlier worked on SaaS security at the onset of the AI wave. 

He teamed up with Vignesh Subbiah, a seasoned engineer and former founding team member at Tophatter and Shogun, who scaled engineering teams and platforms from seed to growth stage. 

After working together at Adobe, the two reconnected to build a system that could meet the urgent security gaps emerging in the new AI stack.

The need became clear quickly. In the early days of GPT-3.5, teams were already sending sensitive prompts into AI tools without oversight, leaking secrets, exposing PII, and consuming costly licenses with no guardrails. Existing DLP tools either blocked the tool altogether or failed to adapt to newer AI workflows.

Unbound takes a different approach. It has already prevented the leakage of 100s of secret credentials, including passwords, API keys, and connection strings, as well as more than 500 instances of personally identifiable information such as customer names, phone numbers, and patient records. 

Rather than simply blocking prompts, Unbound redacts sensitive content in real-time and reroutes high-risk requests to internal, open-source models hosted in the organisation’s cloud. This ensures employees get their answers without ever seeing a security speed bump.

The platform also gives companies fine-grained control over model access and cost. Rather than buying a one-size-fits-all license, teams can allocate premium model access to high-stakes workflows, like engineers building core infrastructure, while routing lighter tasks, like content editing, to smaller open-source models. 

Mid-market customers using Unbound have already saved more than $10,000 annually on unnecessary AI seat licenses. And when new models outperform old ones, as with Gemini 2.5 recently overtaking Claude Sonnet for certain coding tasks, Unbound allows IT to roll them out incrementally, test their effectiveness, and swap them in without breaking employee workflows.

The product is already being used by a growing base of mid-market and enterprise customers across sectors including tech and healthcare. One customer, a leading tech company, recently used Unbound to safely introduce Gemini 2.5 into production AI tools for more than 100 engineers within the same week.

“As AI tools become mainstream, enterprises are turning to flexibility and control,” said Rajaram Srinivasan, co-founder and CEO of Unbound. “They want visibility into what’s being used, assurance that their data is protected, and the ability to swap in better models as the space evolves. Unbound is the bridge that makes that possible.”

Reflecting on Unbound’s early days, CTO and co-founder Vignesh Subbiah said, “Defaulting to blanket bans on AI tools is like being in the times of GPT 3.5. Unbound enables surgical security controls into every AI request so teams can innovate freely without putting corporate secrets at risk.” 

He added, “In just a few months, our customers have prevented over 7,000 potential data leaks and cut AI tooling costs by nearly 70 percent.”

The market is shifting fast. What started as shadow IT is quickly becoming mission-critical infrastructure. Generative AI is embedded in everything from customer support to software engineering, but the tooling around it is still stuck in early-stage chaos.

CIOs and CISOs are looking for ways to support AI adoption without compromising security or governance. Unbound is building that foundation. 

“At THG Ingenuity, we see the security team as an enabler, not a blocker. Unbound empowers us to roll out AI tools to employees with confidence. Unbound AI Gateway’s data protection controls and intelligent routing have been instrumental in safeguarding sensitive data while helping us optimize costs,” says Abraham Ingersoll, chief information security officer (CISO) of The Hut Group (THG), a customer of Unbound.

“AI is projected to reach $4.8 trillion in market value for the enterprise by 2033 globally — but without proper guardrails, that value is at risk. From shadow models to data leaks, the dangers of unmanaged AI are very real.  

“We are excited to back Rajaram Vignesh and the Unbound Security team as they create a new category of AI infrastructure: one built for safety, observability and cost discipline from day one,” said Edith Yeung, general partner at Race Capital. 

“We’re proud to back Rajaram, Vignesh, and the team building a new category of AI infrastructure, one that makes enterprise adoption safe, observable, and cost-efficient from day one.”

Unbound is just getting started. The team plans to expand integrations across the AI ecosystem, deepen model routing capabilities, and support internal model orchestration for enterprises adopting open-source LLMs. Their mission is simple: to ensure every organisation can embrace AI without losing control in the process.

Other investors in the round included Alpha Square Group, Northside Ventures, Liquid2, Pioneer Fund, Scale Asia Ventures, SBXI and notable angels including Ram Shriram (founding board member at Google), Dr Trishan Panch (CSO LuminHealth), Dr John Brownstein (chief innovation officer, Boston Children’s Hospital), Taro Fukuyama (CEO, Fond), Eli Brown (CEO, Guilded, acquired by Roblox), Chris Siakos (CEO Sinefa, acquired by Palo Alto Networks), Joe Vadakkan (CISO, Ex- CRO), Zain Rizavi (Cloudflare, Ridge VC), Finbarr Taylor (CEO, Shogun) alongside other silicon valley and cybersecurity veterans.

This report offers an in-depth exploration of how cyber criminals are weaponising artificial intelligence (AI), alongside strategic insights for defenders to stay ahead.

As AI reshapes industries, it has also erased the lines between truth and deception in the digital world. Cyber criminals now wield generative AI and large language models (LLMs) to obliterate trust in digital identity.

In today’s landscape, what you see, hear, or read online can no longer be believed at face value. AI-powered impersonation bypasses even the most sophisticated identity verification systems, making anyone a potential victim of deception on a scale.

“The swift adoption of AI by cyber criminals is already reshaping the threat landscape,” said Lotem Finkelstein, Director of Check Point Research. “While some underground services have become more advanced, all signs point toward an imminent shift – the rise of digital twins. These aren’t just lookalikes or soundalikes, but AI-driven replicas capable of mimicking human thought and behaviour. It’s not a distant future – it’s just around the corner.”

Key Threat Insights from the AI Security Report:

At the heart of these developments is AI’s ability to convincingly impersonate and manipulate digital identities, dissolving the boundary between authentic and fake.

The report uncovers four core areas where this erosion of trust is most visible:

• AI-Enhanced Impersonation and Social Engineering: Threat actors use AI to generate realistic, real-time phishing emails, audio impersonations, and deepfake videos. Notably, attackers recently mimicked Italy’s defense minister using AI-generated audio, demonstrating that no voice, face, or written word online is safe from fabrication.
• LLM Data Poisoning and Disinformation: Malicious actors manipulate AI training data to skew outputs. A case involving Russia’s disinformation network Pravda showed AI chatbots repeating false narratives 33% of the time, underscoring the need for robust data integrity in AI systems.
• AI-Created Malware and Data Mining: Cyber criminals harness AI to craft and optimise malware, automate DDoS campaigns, and refine stolen credentials. Services like Gabbers Shop use AI to validate and clean stolen data, enhancing its resale value and targeting efficiency.
• Weaponisation and Hijacking of AI Models: From stolen LLM accounts to custom-built Dark LLMs like FraudGPT and WormGPT, attackers are bypassing safety mechanisms and commercialising AI as a tool for hacking and fraud on the dark web.

Defensive Strategies:

The report emphasises that defenders must now assume AI is embedded within adversarial campaigns. To counter this, organisations should adopt AI-aware cyber security frameworks, including:

• AI-Assisted Detection and Threat Hunting: Leverage AI to detect AI-generated threats and artifacts, such as synthetic phishing content and deepfakes.
• Enhanced Identity Verification: Enhanced Identity Verification: Move beyond traditional methods and implement multi-layered identity checks that account for AI-powered impersonation across text, voice, and video—recognising that trust in digital identity is no longer guaranteed.
• Threat Intelligence with AI Context: Equip security teams with the tools to recognise and respond to AI-driven tactics.

“In this AI-driven era, cyber security teams need to match the pace of attackers by integrating AI into their defenses,” added Finkelstein. “This report not only highlights the risks but provides the roadmap for securing AI environments safely and responsibly.”

The full AI Security Report 2025 is available for download here.

It is imperative to place a high emphasis on securing AI models to prevent potential attacks and ensure the uninterrupted operation of essential services.

Safeguarding AI systems against a wide array of threats and vulnerabilities is crucial to maintaining the integrity and reliability of the operations they support.

Consequently, implementing robust and comprehensive security measures is essential to strengthen the future of AI and uphold resilience in the face of potential risks, thereby ensuring the continued reliability and security of critical infrastructure and essential services that rely on AI technology.

Undoubtedly, it is vital to prioritize the security of AI models to prevent them from being attacked. Here are some strategies for securing your AI models:

1. Use encryption:

Implement encryption methods to protect data while it is being processed by the AI model.

This helps to prevent unauthorized access to sensitive information.

2. Implement access control:

Limit access to the AI model and its underlying data to only authorized individuals or systems. Use role-based access controls to ensure that only those with the appropriate permissions can interact with the model.

3. Conduct regular security audits:

Regularly assess the security of your AI models through audits and penetration testing. Identify and address any vulnerabilities to prevent potential attacks.

4. Monitor for anomalies:

Implement monitoring tools to detect any unusual behaviour or anomalies in the AI model’s performance. This can help identify potential attacks or breaches in real time.

5. Update and patch regularly:

Keep the AI model and its underlying systems up to date with the latest security patches and updates. This helps to protect against known vulnerabilities and exploits.

6. Train employees on cybersecurity best practices:

Educate employees on cybersecurity best practices, such as phishing awareness and password security, to prevent human error from compromising the security of the AI model.

7. Implement network security measures:

Protect the network infrastructure that the AI model relies on, such as firewalls, intrusion detection systems, and secure VPN connections.

Permit me to accentuate with instances that in recent years, the integration of artificial intelligence (AI) in critical infrastructure and essential services has expanded significantly as a fortifying the future of robust outcomes of such integration.

Examples include the use of AI in autonomous vehicles, healthcare diagnostics, financial systems, and energy grid management. While these advancements offer numerous benefits, they also present a broader attack surface for potential security breaches.

One notable example is the use of AI in autonomous vehicles. These vehicles rely on sophisticated AI algorithms to interpret sensor data, make real-time decisions, and navigate complex environments. The security of these systems is crucial to prevent potential hacking attempts that could compromise passenger safety.

In healthcare, AI is revolutionizing diagnostics and treatment planning. Machine learning algorithms can process vast amounts of medical data to identify patterns and assist in disease diagnosis.

However, if the security of these AI systems is compromised, there is a risk of tampering with patient records, misdiagnoses, or disruptions in critical medical services.

Financial institutions are also leveraging AI for fraud detection, risk assessment, and customer service.

AI-driven algorithms analyze large volumes of financial transactions to identify potential fraudulent activity.

If these AI systems are not adequately secured, they could be vulnerable to exploitation, leading to financial losses and breaches of customer privacy.

Furthermore, smart energy grids utilize AI for efficient energy distribution and demand forecasting. However, if these AI systems are targeted by malicious actors, there is a risk of interfering with the energy supply, causing widespread power outages, and disrupting essential services.

These examples underscore the critical need to fortify the future by implementing robust security measures for AI systems across various domains.

Strategies such as deploying secure communication protocols, implementing rigorous access controls, and integrating anomaly detection mechanisms can mitigate the risks and enhance the resilience of AI technologies.

The integration of AI into critical infrastructure and essential services necessitates a concerted effort to fortify the future by strengthening AI security.

By proactively addressing potential vulnerabilities and implementing robust security measures, we can safeguard the innovative potential of AI while ensuring a secure technological landscape for the future.

Prioritizing the security of AI models and implementing these strategies enables organizations to reduce the risk of attacks and safeguard their critical data and systems.

Organizations need to prioritize the security of AI models and implement the aforementioned strategies with diligence to minimize the risk of potential attacks.

This approach is pivotal for effectively protecting invaluable data and systems from security breaches and unauthorized access.

On the point regarding encryption. Encryption plays a crucial role in securing AI models by encoding the data and information processed by the AI system.

It ensures that any sensitive data is transformed into an unreadable format, which can only be decrypted and accessed by authorized parties with the appropriate keys or credentials.

Several encryption methods can be utilized to secure AI models, such as symmetric-key encryption, asymmetric-key encryption, and homomorphic encryption.

Symmetric-key encryption uses a single key to both encrypt and decrypt the data, while asymmetric-key encryption utilizes a pair of public and private keys.

Homomorphic encryption enables computations to be performed on encrypted data without the need for decryption, which is particularly useful for protecting sensitive information during AI model training and inference.

By implementing encryption, organizations can safeguard sensitive data as it passes through the AI model, preventing unauthorized access and maintaining data confidentiality.

This is especially important in scenarios where AI models handle personal, financial, or proprietary information, as it helps to maintain trust and compliance with privacy regulations.

Additionally, encryption can also be used to protect the model parameters and architecture, preventing them from being reverse-engineered or tampered with by malicious actors.

Overall, encryption is a fundamental security measure for safeguarding AI models and the data they process.

Essentially, organizations need to recognize that AI security is an ongoing process, and proactive measures need to be continuously integrated into the operational framework. As the threat landscape continues to evolve, AI models must adapt to new potential risks and vulnerabilities.

This requires a comprehensive and dynamic approach to security, characterized by continuous monitoring, adaptation, and improvement.

Further, organizations should take steps to foster a culture of cybersecurity awareness and vigilance among their employees.

Training programs and awareness initiatives can empower personnel to recognize and respond to potential security threats, reducing the likelihood of human error compromising AI model security.

Besides, collaboration and information-sharing within the industry can contribute to bolstering AI security.

By participating in sharing threat intelligence, best practices, and emerging trends in AI security, organizations can collectively enhance their defences and fortify their AI systems against a rapidly evolving threat landscape.

In conclusion, the safeguarding of AI models demands a multi-faceted, resilient, and agile security posture, underpinned by comprehensive measures, continuous improvement, and collaboration across the industry.

By embracing these principles and approaches, organizations can instil trust, reliability, and resilience in their AI implementations, ensuring the protection of valuable data and systems against potential attacks.

Through unwavering commitment and proactive measures, the security of AI models can be upheld in the face of emerging cyber threats, enabling organizations to navigate the evolving landscape of AI security with confidence and resilience.

*The writer, Professor Ojo Emmanuel Ademola, is the first Nigerian Professor of Cyber Security and Information Technology Management

In the world of cyber security, it has become accepted that new technologies are crucial to keeping up with sophisticated cyber criminals hiding deep within the web.

In the physical world, security also needs to keep pace with increasing crime and more sophisticated criminals.

It’s here that the intersection between the physical and virtual world occurs. The advent of cloud computing opened a whole new world of surveillance, sensors, monitoring, and analytics.

The speed of innovation means that solutions continue to evolve, whether in the form of off-site monitoring, pro-active security solutions powered by artificial intelligence, access control and more.

2023 will see more of this, as the leading security service providers and vendors seek to bring the best products to market.

Guardian Eye, for example, will be bringing night cameras that can see and record in colour. This is a fundamental shift from the mainstay of black and white, or saturated, images. The implications for investigations are huge, and this technology represents the next frontier in camera monitoring.

Behind the cameras, indeed behind any IoT sensor or device, is the power that cloud-based analytics can bring to security solutions. Again, speaking for ourselves, we are constantly working on our algorithms and researching the best that technology can bring to ensure monitoring and analytics continues evolving to increase security capabilities.

It is an exciting world, where analytics can provide a situational awareness not seen before so that people and property can enjoy proactive protection because of technology’s ability to pick up trends and anomalies in near real-time.

The ability to review hours upon hours of video footage in minutes and pick up patterns or red flags is priceless, and can rapidly speed up response and investigations.

Ultimately, the ability to recognise and pick up patterns turns a surveillance camera into a super guard working in real-time. Expect to see more development in this field throughout the year.

However, from a South African perspective, the single biggest trend in the world of security is going to be how service providers and vendors come up with solutions that get around load shedding. From a security perspective, load shedding presents a massive risk.

Many alarm systems, electric fences, automatic gates, surveillance systems and access control solutions have some degree of backup in place. However, the battery back-ups in these solutions, especially electric fences, alarm systems and automatic gate motors, weren’t designed to withstand extended periods of no power that occur regularly.

These batteries are becoming damaged as they are depleted and sometimes not fully charged before the next power cut.

The result is that many residences and homes have to deal with literal blackouts, where security is non-existent, a lot sooner than they would have before this latest, extended bout of load shedding.

Security service providers and vendors are working day and night to find ways to ensure that their solutions are up and running, from surveillance, to access control, to proactive and reactive security products.

From a Guardian Eye perspective, we are part of the Vivica Group, and our sister company, Stage Zero, which will be launched to the public soon, provides us with a competitive advantage.

Through our sister company, we have access to innovative battery backup systems, and solar and wind generation capability, which we are currently exploring to ensure that our solutions and offerings continue as normal despite Eskom’s woes. The solution simply must be that load shedding should not affect security, anywhere, in any way.

Stage Zero, on the other hand, gains access to Guardian Eye’s nerve centre which equips it with state-of-the-art monitoring and analytics for its wide suite of solutions that will be deployed widely.

Ultimately, the differentiator between security providers lies in how they leverage the latest technology to answer real, pressing challenges on the ground.

In the case of South African challenges, security providers need to not only ensure their products leverage the best technology such as AI-driven analytics capabilities, but also that they invest in the best backup and renewable energy technology to ensure their products and services are accessible when the power is out.