Getting tired of the risk of cyberattacks from criminals looking to take over your smart building's IoT, corrupt embedded AI data, and extort your real estate business? IoE Corp has a decentralized solution that promises security beyond cyber and even helps you sleep better at night.
Once only a concept associated with science fiction, AI (Artificial Intelligence) and machine learning are being adopted in the healthcare industry, physical fitness and wellness, and not the least: real estate. The global market size of AI in real estate will grow to $737 billion in 2027, while the overall AI spending will be $97.9 billion by 2023, 2.5 times more than in 2019.
And the reason is simple. AI, especially embedded AI, and real estate seems to be a perfect match. Embedded AI is used in various ways in the real estate industry to improve efficiency, reduce costs, and enhance the overall customer experience.
AI algorithms can analyze data on property sales, rental rates, and other market indicators to determine the value of a property. This helps real estate professionals make more informed pricing decisions and ensures that properties get priced accurately. Real estate companies also use embedded AI to monitor building systems and predict when maintenance is needed. This way, they can prevent costly breakdowns and ensure that buildings are always in good working order.
Except for this, AI algorithms are used to analyze customer data and recommend properties that are likely to meet their needs and preferences, something that increases customer satisfaction and improves the chances of making a sale. For the customer, on the other hand, embedded AI automates various functions in smart homes, such as adjusting the temperature, turning on lights, and locking doors, a convenience for homeowners that helps save energy.
No business solution comes without challenges and some risks embedded AI poses to the real estate business. One of the biggest hazards of embedded AI in real estate is the risk of bias in AI algorithms. If the data used to train an AI is biased, the algorithm may perpetuate and even amplify that bias.
For example, if an AI is trained using historical data that reflects discriminatory practices in the real estate industry, the algorithm may produce biased results that perpetuate that discrimination. This can lead to unfair treatment of particular groups of people, which can harm the reputation of the real estate business and even result in legal action.
There are also legal and regulatory risks, the danger of getting overly dependent on technology, as well as possible problems with a lack of transparency toward customers.
But most alarming of all is the potential threats and data breaches from cybercriminals. Those underlying security risks cast a long dark shadow over the bright future of embedded AI and IoT (Internet of Things) systems in housing. On top of that, there is also the question of how to guarantee customer data privacy.
The primary security problem is that embedded AI relies on large amounts of data to train its algorithms, often including sensitive information such as customers' personal and financial data. If the data is not adequately secured, it can be vulnerable to data breaches. That's why real estate businesses must take appropriate measures to protect customer data from unauthorized access, theft, and misuse.
Cybercriminals and cyberterrorists are looking at IoT devices, embedded data within them, and their installations as an opportunity to steal information, extort money and attack critical infrastructure. One of the most simple but efficient attacks is the DDoS (Distributed Denial of Service) attack, a simple way to take out and extort cloud-based services. There is also targeted ransomware, common malware, DNS hijacking, and other tools these criminals and terrorists use.
The common trait between these attacks is that they, in one way or another, exploit centralized infrastructure solutions, be it at the index, data, service, or user level. And it all works since almost all IoT networks, and the data from their embedded AI systems are stored centrally in clouds.
To believe that cybersecurity in its current form of attacker vs. defender will work when any takeout of data flow or service is directly and immensely disruptive to our economy, security, and lives is not to take these risks seriously.
At IoE Corp, we have instead devised a decentralized solution called Eden. Our Eden system entirely negates these risks. Using data verification via blockchain consensus with data validation and sanity checks on AI, bad hardware and malware are detected and managed on the fly. We make the real estate industry and embedded AI become that perfect match.
The Eden system is based on scalable device clustering, making adding new devices as nodes easy. It is possible for any device to contribute computing resources over an intelligent mesh network so that computing can happen where it's needed and close to where it will be used.
IoE Corp, in addition, has developed quantum-safe tunnels using polymorphic encryption keys and uses a blockchain with consensus to verify the data moved between the nodes over the tunnels, thus creating trusted data walled gardens. The orchestration of computing and storage is done via service manifests that describe service rules, policies, and logic; an autonomous knowledge-based AI manages the underlying orchestration mechanics using network consensus over the blockchain as a deciding mechanism.
By deploying the Eden System, you minimize the security risks connected to IoT and embedded AI. You will even sleep better at night knowing your system is secure. No more nightmares about cyberattacks from malign hackers; you can guarantee that you secure customer data.
Read more: https://ioecorp.com/
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