Consumer privacy has made big headlines in the recent years with the Facebook Cambridge Analytica Scandal, Europe's GDPR and high-profile breaches by companies like Equifax. It's clear that the data of millions of consumers is at risk every day, and that companies that wish to handle their data must do so with the highest degree of protection around both security and privacy of that data, especially for companies that build and sell AI-enabled facial recognition solutions. As CEO of an AI-enabled software company specializing in facial recognition solutions, I've made data security and privacy among my top priorities. Our pro-privacy stance goes beyond mere privacy by design engineering methodology. We regularly provide our customers with education and best practices, and we have even reached out to US lawmakers, lobbying for sensible pro-privacy regulations governing the technology we sell.
The development and deployment of artificial intelligence (AI) tools should take place in a socio-technical framework where individual interests and the social good are preserved but also opportunities for social knowledge and better governance are enhanced without leading to the extremes of'surveillance capitalism' and'surveillance state'. This was one of the main conclusions of the study'The impact of the General Data Protection Regulation on Artificial Intelligence', which was carried out by Professor Giovanni Sartor and Dr Francesca Lagioia of the European University Institute of Florence at the request of the STOA Panel, following a proposal from Eva Kaili (S&D, Greece), STOA Chair. Data protection is at the forefront of the relationship between AI and the law, as many AI applications involve the massive processing of personal data, including the targeting and personalised treatment of individuals on the basis of such data. This explains why data protection has been the area of the law that has most engaged with AI and, despite the fact that AI is not explicitly mentioned in the General Data Protection Regulation (GPDR), many provisions of the GDPR are not only relevant to AI, but are also challenged by the new ways of processing personal data that are enabled by AI. This new STOA study addresses the relation between the GDPR and AI and analyses how EU data protection rules will apply in this technological domain and thus impact both its development and deployment.
Undoubtedly, artificial intelligence (AI) is able to support organisations in tackling their threat landscape and the widening of vulnerabilities as criminals have become more sophisticated. However, AI is no silver bullet when it comes to protecting assets and organisations should be thinking about cyber augmentation, rather than just the automation of cyber security alone. Areas where AI can currently be deployed include the training of a system to identify even the smallest behaviours of ransomware and malware attacks before it enters the system and then isolate them from that system. Other examples include automated phishing and data theft detection which are extremely helpful as they involve a real-time response. Context-aware behavioural analytics are also interesting, offering the possibility to immediately spot a change in user behaviour which could signal an attack.
Training a machine learning model requires a large quantity of high-quality data. One way to achieve this is to combine data from many different data organizations or data owners. But data owners are often unwilling to share their data with each other due to privacy concerns, which can stem from business competition, or be a matter of regulatory compliance. The question is: how can we mitigate such privacy concerns? Secure collaborative learning enables many data owners to build robust models on their collective data, but without revealing their data to each other.
It is true to say that AI and ML offer great promise when it comes to organisational security measures. A predictive security stance may be some way off for many businesses and the belief that AI or ML will dissolve existing poor practice or protocols is as widespread as it is erroneous. Before really talking about AI and ML, we must talk about bias and the impact it has on quality outcomes from either technology. Bias will simply double down on any practice or protocol in place and reinforce it, good or bad. You don't have to look very far to see an example of how it can go wrong if you have not considered the bias problem – Amazon was forced to scrap its experimental AI recruitment tool, as it eventually decided the best people for its roles were pretty much just men.
Secretary of State Mike Pompeo seized on a U.N. report confirming Iranian weapons were used to attack Saudi Arabia in September and were part of an arms shipment seized months ago off Yemen's coast; State Department correspondent Rich Edson reports. A fire and an explosion struck a centrifuge production plant above Iran's underground Natanz nuclear enrichment facility early Thursday, analysts said, one of the most-tightly guarded sites in all of the Islamic Republic after earlier acts of sabotage there. The Atomic Energy Organization of Iran sought to downplay the fire, calling it an "incident" that only affected an under-construction "industrial shed," spokesman Behrouz Kamalvandi said. However, both Kamalvandi and Iranian nuclear chief Ali Akbar Salehi rushed after the fire to Natanz, a facility earlier targeted by the Stuxnet computer virus and built underground to withstand enemy airstrikes. The fire threatened to rekindle wider tensions across the Middle East, similar to the escalation in January after a U.S. drone strike killed a top Iranian general in Baghdad and Tehran launched a retaliatory ballistic missile attack targeting American forces in Iraq. While offering no cause for Thursday's blaze, Iran's state-run IRNA news agency published a commentary addressing the possibility of sabotage by enemy nations such as Israel and the U.S. following other recent explosions in the country.
TOKYO, June 30, 2020 /PRNewswire-PRWeb/ -- About Cyneural While cyber-attack defenses generally respond by detecting specific patterns of "signatures" that indicate malicious access, complex or unknown attacks that utilize AI or BOTs can be difficult to detect or can result in false positives. This is why cyber-attack defenses also need to take advantage of technology with flexibility such as AI. Against this backdrop, Cyber Security Cloud developed its own attack detection AI engine, Cyneural, in August 2019. "Cyneural" uses a feature extraction engine that utilizes the knowledge cultivated through CSC's research on web access and various attack methods. It builds multiple types of training models to help detect not only common attacks but also unknown cyber-attacks and false positives at a higher speed. About Cyneural being used in Shadankun and WafCharm Since the development of Cyneural, CSC has been operating it by utilizing the large amount of data that they have.
As businesses, governments and consumers rely on digital systems to fulfil most of their daily operations, so do the risks of those systems being hacked increase. The more the technologies they adopt, the greater the hazards they have to face. In fact, new solutions to ease businesses daily operations such as Artificial Intelligence in Operative Systems and IT software huge databases, bring even more complexity to an already convoluted world. However, these new techs can also become their strongest allies! If properly developed and embraced, they can deliver new layers of security that build up a strong shield of protection against hackers.
Whether or not your organisation suffers a cyber attack has long been considered a case of'when, not if', with cyber attacks having a huge impact on organisations. In 2018, 2.8 billion consumer data records were exposed in 342 breaches, ranging from credential stuffing to ransomware, at an estimated cost of more than $654bn. In 2019, this had increased to an exposure of 4.1 billion records. While the use of artificial intelligence (AI) and machine learning as a primary offensive tool in cyber attacks is not yet mainstream, its use and capabilities are growing and becoming more sophisticated. In time, cyber criminals will, inevitably, take advantage of AI, and such a move will increase threats to digital security and increase the volume and sophistication of cyber attacks.
As the world becomes increasingly digital, we are unlocking more value and growth than ever before. However, a challenge that governments, enterprises and well as individuals leveraging technology are constantly facing is the growing threat of cyberattacks that looms large over us. Cyber security solutions provider SonicWall's 2019 report revealed 10.52 billion malware attacks in 2018, a 217% increase in IoT attacks and 391,689 new variants of attack that were identified. What's more is that cyber criminals today are evolving with technology and upping their game. Such incidents don't just have the potential to bring businesses to a standstill but can also inflict serious damages to their resources and repute.