Mediaboss Marketing

TURIN, Italy--(BUSINESS WIRE)--Today, cybersecurity represents an essential priority in the implementation of new technologies, especially given the crucial role that they have come to play in our private and professional lives. Smart Homes, Connected Cars, Delivery Robots: this evolution will not stop and so, in tandem, it will be necessary to develop automated and AI-based solutions to combat the growing number of security threats. The risks from these attacks are attributable to several factors, such as increasingly complex and widespread digital networks and a growing sensitivity to data privacy issues. These are the themes that emerge from the new Cybersecurity Automation research conducted by Reply, thanks to the proprietary SONAR platform and the support of PAC (Teknowlogy Group) in measuring the markets and projecting their growth.

In particular, the research estimates the principal market trends in security system automation, based on analysis of studies of the sector combined with evidence from Replys own customers. The data compares two different clusters of countries: the Europe-5 (Italy, Germany, France, the Netherlands, Belgium) and the Big-5 (USA, UK, Brazil, China, India) in order to understand how new AI solutions are implemented in the constantly evolving landscape of cybersecurity.

As cyberattacks like hacking, phishing, ransomware and malware have become more frequent and sophisticated, resulting in trillions of euros in damages for businesses both in terms of profit and brand reputation, the adoption of hyperautomation techniques has demonstrated how artificial intelligence and machine learning represent possible solutions. Furthermore, these technologies will need to be applied at every stage of protection, from software to infrastructure, and from devices to cloud computing.

Of the 300 billion in investments that the global cybersecurity market will make in the next five years, a large part will be directed toward automating security measures in order to improve detection and response times to threats in four different segments: Application security, Endpoint security, Data security and protection, Internet of Things security.

Application Security. Developers who first introduced the concept of security by design, an adaptive approach to technology design security, are now focusing on an even closer collaboration with the operations and security teams, termed DevSecOps. This newer model emphasizes the integration of security measures throughout the entire application development lifecycle. Automating testing at every step is crucial for decreasing the number of vulnerabilities in an application, and many testing and analysis tools are further integrating AI to increase their accuracy or capabilities. Investments in application security automation in the Europe-5 market are expected to see enormous growth, around seven times the current value, reaching 669 million euros by 2026. A similar growth is forecast in the Big-5 market, with investments rising to 3.5 billion euros.

Endpoint security. Endpoints, such as desktops, laptops, smartphones and servers, are sensitive elements and therefore possible sources of entry for cyberattacks if not adequately protected. In recent years, the average number of endpoints within a company has significantly increased, so identifying and adopting efficient and comprehensive protection tools is essential for survival. Endpoint detection and response (EDR) and Extended detection and response (XDR) are both tools created to accelerate the response time to emerging security threats, delegating repetitive and monotonous tasks to software that can manage them more efficiently. Investments in these tools are expected to increase in both the Europe-5 and Big-5 markets over the next few years, reaching 757 million euros and 3.65 billion euros respectively. There are also a multitude of other tools and systems dedicated to incident management that can be integrated at the enterprise level. For example, in Security Orchestration Automation and Response (SOAR) solutions, AI can be introduced in key areas such as threat management or incident response.

Data security and protection. Data security threats, also called data breaches, can cause significant damage to a business, resulting in risky legal complications or devaluating brand reputation. Ensuring that data is well-preserved and well-stored is an increasingly important challenge. It is easy to imagine how many different security threats can come from poor data manipulation, cyberattacks, untrustworthy employees, or even just from inexperienced technology users. Artificial intelligence is a tool for simplifying these data security procedures, from discovery to classification to remediation. Security automation is expected to reduce the cost of a data breach by playing an important role in various phases of a cyberattack, such as in data loss prevention tools (DLP), encryption, and tokenization. In an effort to better protect system security and data privacy, companies in the Europe-5 cluster are expected to invest 915 million euros in data security automation by 2026. The Big-5 market will quadruple its value, reaching 4.4 billion euros in the same timeframe.

Internet of Things security. The interconnected nature of IoT allows for every device in a network to be a potential weak point, meaning even a single vulnerability could be enough to shut down an entire infrastructure. By 2026, it is estimated that there will be 80 billion IoT devices on earth. The impressive range of abilities offered by IoT devices for different industries, though enabling smart factories, smart logistics, or smart speakers, prevents the creation of a standardized solution for IoT cybersecurity. As IoT networks reach fields ranging from healthcare to automotive, the risks only multiply. Therefore, IoT security is one of the most difficult challenges: the boundary between IT and OT (Operational Technology) must be overcome in order for IoT to unleash its full business value. As such, it is estimated that the IoT security automation market will exceed the 1-billion-euro mark in the Europe-5 cluster by 2026. In the Big-5 market, investments will reach a whopping 4.6 billion euros.

Filippo Rizzante, Replys CTO, has stated: The significant growth that we are witnessing in the cybersecurity sector is not driven by trend, but by necessity. Every day, cyberattacks hit public and private services, government and healthcare systems, causing enormous damage and costs; therefore, it is more urgent than ever to reconsider security strategies and reach new levels of maturity through automation, remembering that though artificial intelligence has increased the threat of the hacker, it is through taking advantage of AIs opportunities that cyberattacks can be prevented and countered.

The complete research is downloadable here. This new research is part of the Reply Market Research series, which includes the reports From Cloud to Edge, Industrial IoT: a reality check and Hybrid Work.

ReplyReply [EXM, STAR: REY, ISIN: IT0005282865] is specialized in the design and implementation of solutions based on new communication channels and digital media. Reply is a network of highly focused companies supporting key European industrial groups operating in the telecom and media, industry and services, banking, insurance and public administration sectors in the definition and development of business models enabled for the new paradigms of AI, cloud computing, digital media and the Internet of Things. Reply services include: Consulting, System Integration and Digital Services. http://www.reply.com

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Reply: Automation and Artificial Intelligence Are the Strategic Keys for an Effective Defense Against Growing Threats in the Digital World - Business...

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Grace' is a lifelike robot nurse, built withartificial intelligence to bring emotional care for patients duringthe pandemic and make them feel comfortable and at ease.

Artificial intelligence (AI), the self-didactic technology whichdetects patterns from historical data, is pervading all walks oflife, be it healthcare or the financial services industry.

The High-Level Expert Group on AI, tasked by the EuropeanCommission to draft AI ethics guidelines, defined AI assystems that display intelligent behaviour byanalysing their environment and taking actions with somedegree of autonomy to achieve specificgoals''.

In the finance world, AI has evolved substantially over therecent decades and its utility ranges from the performance datamonitoring, establishing creditworthiness and credit scoring, aswell as in combatting cybercrime and money laundering. However, theexponential use does not come without a fair amount of risksattached, in particular in machine learning applications whererisks of data bias can lead to erroneous results being generated bythe AI due to statistical errors or interference during the machinelearning process.

The paucity in AI regulation and the multiplicity in AIpractices led the European Commission to focus on this technologyin its Digital Finance Package, launched at the end of 2020 toensure that the EU financial sector remains competitive whilecatering for digital financial resilience and consumerprotection.

Towards the end of last year, the European Central Bankpublished its opinion welcoming the Artificial Intelligence Act.While noting the increased importance of AI-enabled innovation inthe banking sector, given the cross-border nature of suchtechnology, the supranational body held that the ArtificialIntelligence Act should be without prejudice to the prudentialregulatory framework to which credit institutions are subject.

The ECB acknowledged that the proposal cross-refers to theobligations under the Capital Requirements Directive (2013/36 orCRD V') including risk management and governanceobligations to ensure consistency. Yet the ECB sought clarificationon internal governance and outsourcing by banks who are users ofhigh-risk AI systems.

Raising its concerns as to its role under the new ArtificialIntelligence Act, the ECB reiterated that its powers derive fromarticle 127(6) of the Treaty on the Functioning of the EuropeanUnion (TFEU) and the Single Supervisory Mechanism regulation (EU)1024/2013 (SSM regulation), which instruments confer on the ECBspecific tasks concerning prudential supervision policies of creditinstitutions and other financial institutions.

Recital 80 of the proposal provides thatauthorities responsible for the supervision andenforcement of the financial services legislation, including whereapplicable the European Central Bank, should be designated ascompetent authorities for the purpose of supervising theimplementation of this regulation, including for marketsurveillance activities, as regards AI systems provided or used byregulated and supervised financial institutions.

The bank held that market surveillance' under theArtificial Intelligence Act would also consist in ensuring thepublic interest of individuals (including health and safety). In anutshell, the ECB informed the Commission that the ECB has nocompetence to regulate solutions like Grace the robot, but it willonly ensure the safety and soundness of credit institutions. Tothis effect, the bank suggested that (i) a relevant authority beappointed for health and safety risks related obligations; and (ii)another AI authority be set up at Union level to ensureharmonisation.

In parallel, the ECB also recommended that the ArtificialIntelligence Act be amended so as to mandate that, that in relationto credit institutions evaluating the creditworthiness of personsand credit scoring, an ex-post assessment be carried out by theprudential supervisor as part of the SREP, in addition to theex-ante internal controls that are already listed in theproposal.

Interestingly, the Bank for International Settlements, in itsnewsletter on artificial intelligence and machine learning, raisedits concerns in view of the cyber, security and confidentialityrisks, data governance challenges, risk management, biases,inaccuracies and potential unethical outcomes of AI systems,the committee believes that the rapid evolution anduse of AI/ML by banks warrant more discussions on the supervisoryimplications.

While the Artificial Intelligence Act has not been agreed uponin its final form and may be substantially changed before itsacceptance, it is safe to say that the financial sector is one inwhich the challenges relating to the use of AI need to be evaluatedwell, before and when deploying such technological solutions, inview of the risks and individual rights that are at stake.

Originally Published by Times of Malta

The content of this article is intended to provide a generalguide to the subject matter. Specialist advice should be soughtabout your specific circumstances.

Link:
ECB Publishes Its Bug Report On The Proposed EU Artificial Intelligence Act - New Technology - Malta - Mondaq

In marketing, 'creative impact' was once too subjective to measure. But with modern AI solutions, creative efforts can now be effectively analyzed. As part of The Drum's Creativity in Focus Deep Dive, Meta's Maria Pavlova (marketing science partner), Karen Chui (creative partner manager, EMEA) and Safia Dawood (marketing science partner) look at how AI tools can help marketing professionals develop more effective advertising strategies through creative effectiveness insights.

Data-driven optimization is widely adopted in digital advertising. Todays marketing professionals have perfected audience profiles and campaign logistics. Yet, the uncomfortable truth is that creative effectiveness is often overlooked despite being the most important campaign element. Creative quality accounts for 70% of an ads success, eclipsing procedural elements like flighting or platform choice.

Audiences want to be dazzled and amazed, so its important to offer them exciting content. Unfortunately, only 60% of advertisers measure creative effectiveness. Of those, the majority collect feedback using post-campaign focus groups or surveys, making the data somewhat unreliable.

At first glance, creative effectiveness appears too subjective to measure. However, todays AI (artificial intelligence) solutions can effectively analyze your creative impact and are becoming increasingly accessible to marketers.

Solutions from Meta are optimizing campaigns creative prowess and advancing AI-enabled creative workflows. Read on to learn how, and discover case studies on brands that gained sales using creative effectiveness insights.

Cutting-edge marketers already use AI to maximize the potential within their advertising strategy. AI simplifies the creative process by helping to produce new creative assets and adjust existing ones at scale.

For example, automated software can translate content across languages, crop images to frame subjects better and generate image descriptions to make visual assets more accessible. As a result, marketing professionals are uncovering new artistic heights and reimagining how to convey messages across mediums.

However, AI is able to take an even larger role within the advertising life cycle and assist marketing teams in planning and optimizing content like never before.

AI tools can help marketing professionals develop more effective advertising strategies based on audience interaction data. This continuous approach to ad optimization is the future of marketing and will help brands lead with content that audiences love.

Measuring creative effectiveness begins with Illumination, which provides initial insights and builds a model to predict future campaign performance. The results help uncover your next big campaign idea and guide your creative process in later stages.

Algorithms begin by analyzing anonymized and aggregated historical data on consumer behavior and identifying which creative elements are most common among your highest-performing ads.

The result is a framework that helps marketers compare different advertising approaches and discover the most effective one before the campaign begins. In turn, marketing professionals can generate new creative assets with more confidence and exceed audience expectations more effectively.

AI can also help steer the ongoing creative strategy once your campaign begins.

Changes within the digital ecosystem mean advertisers must adopt a new playbook. By leveraging continuous optimization through a test-and-learn process, marketers can focus on what audiences like rather than who they are, and avoid privacy concerns from targeted advertising.

Performance evaluation using machine learning can highlight the potential of each creative element within your existing assets. Automated testing solutions can also enable brands to experiment with dynamic business outcomes more efficiently. As a result, creative teams can devise more effective strategic approaches while campaigns are in flight.

Businesses with multiple locations often have to balance variations in brand guidelines to connect with local audiences effectively. Consistent color palettes and tones of voice help brands feel unified and cultivate a consistent public image.

Yet, these variations present a challenge to marketers as communication must be consistent over time.

Todays AI tools measure how creative assets and copy fit within existing brand guidelines and identify areas for improvement. These insights help brands stay true to their guidelines, both between and within sub-brands, and ensure that public-facing communications are curated and consistent.

As a leading beer brand, Heineken needed a way to assess its creative effectiveness across its 300+ sub-brands. By partnering with Meta and CreativeX, a creative analytics company, Heineken uncovered new insights into its creative effectiveness and strategic potential.

[The] creative elements we measured are foundational to driving effectiveness in the Metaecosystem without restricting the creative process and flow, says Sander Bosch, global CMI manager communication effectiveness at Heineken.

Dominos Pizza is one of the worlds most recognized pizza brands, offering a seemingly infinite number of combinations of toppings and crust fillings.

Dominos wanted to improve their video creative using technology and AI and turned to Spirables creative intelligence suite. The two worked together to create three unique ad variants for a multi-cell split test on Facebook and Instagram across a four-week period.

Spirable determined that while motion was beneficial, moving the pizza out of the frame was not.

By utilizing these new insights in future ads, Dominoes increased return on ad spend by 20%, click-through rate by 6%, and reduced cost per result by 9%.

Have you considered how to measure your creative effectiveness?

Creative effectiveness is just the solution to help marketers measure campaign performance and brand recognition in the transition away from targeted online advertising. AI tools from Meta, CreativeX, Spirable, and others are available today and can elevate your creative effectiveness and remove the guesswork from future ad strategies.

By maximizing your creative effectiveness, you can mitigate signal loss and deliver high-impact content to followers at the same time. You can also establish creative best practices for future campaigns and curate your brand portfolio more effectively.

Moreover, automating routine creative tasks using AI allows you to focus on your campaigns long-term health and ensure ad budgets go further.

Discover tools that can help your business improve creative effectiveness with key insights from Meta Foresights.

Read more here:
How Artificial Intelligence Is Helping To Measure Creative Effectiveness In Marketing - The Drum

Artificial intelligence (AI) is on the priority list for every executive who uses technology to enable their business. And today, every business is a technology business. Despite the excitement around AI and investments in its capabilities, only about a third of companiessay theyve adopted leading operational practices for AI but an increasing percentage are working toward that goal.

While AI is often seen as the golden ticket to take business operations into the 21st century and it can to do so, the technology must be approached specifically and strategically, not as an all-in-one solution.

In the universe of technology, one can picture a solar system of interdependent capabilities. At the core, cloud technology serves as the sun a central power source fuelling and enabling other technologies. Underlying cloud platforms, such as Amazon Web Services or Google Cloud, provide the basis for other capabilities to flourish in the technology universe.

Rotating around cloud platforms, there are various AI planets in orbit that build off of cloud infrastructure to deliver solutions such as automation, machine learning, robotic process automation, and more. Many business leaders are eager to enter the orbit of artificial intelligence solutions, but must first start by building the necessary foundation for successful AI implementations.

Once the centre of the AI solar system is in place, to effectively unlock the power of AI, its important that business leaders understand what it is they are trying to solve. And while many suppliers have powerful offerings, AI is not one-size-fits-all in its approach or implementation. It takes several capabilities and applications to drive true end-to-end AI outcomes.

This ecosystem strategy can ultimately offer flexibility and stability for IT decision-makers looking to harness business data and drive meaningful results for their organisations. Key to demonstrating the importance of AI ecosystems is discussing current barriers a company is trying to overcome and what specific AI capabilities will solve for them.

Today, business leaders are looking to define the function of artificial intelligence in their organisations and how they can effectively implement AI given their current technology stacks.

For example, a banking executive may look to automate some of their companys digital banking capabilities. To get there, the institution must consider how they are currently housing their data, how that data will be processed and then refined for usage, and finally how the data can provide insight to their workforce and what insights will be most valuable to them.

In this case, an organisation may have to consider combining the technology and environment they have in place with new technology and capabilities to achieve their desired outcome of a new automated banking tool. The allure of a one-stop shop for AI needs may sway businesses to heavily invest in one provider, which can put up roadblocks on the journey to a meaningful, AI-powered solution.

Part of the trouble with seeing one supplier as a silver-bullet solution is that businesses may invest too heavily in a provider that wont help them move the needle on all of their specific AI goals. Given the hefty budgets businesses are developing for their IT departments, its critical to understand that investments are going towards the appropriate solution(s) and that more money towards a nebulous, blanket AI may not always equate to unlocking business success.

IT decision-makers must have a clear understanding of their companys technology solar system before implementing a new AI tool Anthony Ciarlo and Frank Farrell, Deloitte

Moreover, the overarching cloud environment in which an AI solution is deployed can make or break its success. This means IT decision-makers must have a clear understanding of their companys technology solar system before implementing a new AI tool. When AI-related requests for proposal come across our desks, our first goal is to work through the specific needs of the clients organisation and if the resources they are putting behind the AI solutions will get them where they want to be.

End to end, it is difficult for any one supplier to meet all of the AI needs of an organisation. Some are leaders in automation, while others are leaders in data analytics or machine learning understanding these different strengths enables Deloitte to provide meaningful, tailored assessments as to what investments should be made.

As a systems integrator, once the Deloitte team has holistic insight into an organisations pain points, it can provide confident recommendations as to where money should be invested and how companies can see the greatest return on investment in their technology budgets. The Deloitte team delivers confidence in integrating and navigating the solar system to provide the desired outcomes its clients and their clients need.

The ecosystem approach to AI solutions marks an important shift for how systems integrators should be approaching their client solutions. In years to come, its likely that there will be increased collaboration across market providers, resulting in more streamlined, transparent AI implementation processes.

The key driver for this shift is continued conversations with business and technology leaders who understand that AI is not an isolated entity, but rather serves as a key component within a solar system of interconnected platforms and tools that can offer individualised solutions for the most pressing business challenges.

Anthony Ciarlo is strategy and analytics alliances leader and Frank Farrell is principal for cloud analytics and AI ecosystems at Deloitte.

Read more:
How to leverage the artificial intelligence solar system - ComputerWeekly.com

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The United States spends more on health care than any other country in the world.

But Americans aren't as healthy as people living in other developed countries.

Could artificial intelligence change all that?

WBUR's On Point brings you Smarter health, a four-part series exploring how artificial intelligence and machine learning may revolutionize the health care industry.

We'll investigate the technology already available, or in development, for clinical settings, examine the ethical dilemmas the technology presents in medicine and understand the guiderails and regulations in progress to advise AI advancements.

We'll also hear from the people involved in AI in health care; scientists developing tools, clinicians and doctors using the tools, and patients experiencing changing technology as part of their care.

Episode 1. How AI is transforming health care: Artificial intelligence offers the potential to improve health care from predicting someones risk of having a heart attack, to predicting seizure loads for epilepsy patients, to solving public health problems. What is the potential for AI to transform American health care?

Episode 2. Ethics of the death predictor: We'll break down the ethical considerations of AI in health care.What are the privacy concerns about data collection, and how can researchers and developers advance tools while protecting patients?

Episode 3. Regulating the algorithm:As AI develops in the health care space,regulations need to develop in tandem. We'll talk to the head of the FDAsdigital health division, Dr. Matthew Diamond, about what role the FDA will play as AI expands. Well also talk to experts about guardrails needed to ensure patient safety and privacy.

Episode 4. The people of AI: Our final episode gets up close with the people working and developing AI technology, and the patients receiving AI care. How can this technology thrive in our complex and broken health care system?

Radio

Podcast

Got a question about how AI will impact how you receivehealth care? Or maybe you're a scientist, doctor or patient with an AI story to share? Leave us a voicemail at 617-353-0683.

Meghna Chakrabarti is the award-winning host and editor ofOn Point. Based in Boston, she is on the air Monday through Friday.

The Alliance for Women in Media honoredOn Point'sepisode"A Look Back at 1992 Los Angeles And America Since Rodney King"with a 2022 nationalGracie Award for Best News Documentary. The Alliance for Women in Media also gave Meghna anhonorable mentionfor best nationally syndicated non-commercial correspondent/host.

On Point'sepisode on Los Angeles since Rodney King also won a2022 regional Edward R. Murrow awardfor best news documentary. In 2021,On Pointwon aNational Edward R. Murrow awardfor best news documentary for"What the President Knew."The show examined presidential decision-making before 9/11 and the COVID pandemic.

Chakrabarti is the former host ofRadio Boston, WBURs acclaimed weekday local show. She's the former host ofModern Love: The Podcast, a collaboration of WBUR and The New York Times (2016-2020) and was the primary fill-in host forHere & Now, NPR and WBUR's midday show. She reported on New England transportation and energy issues for WBURs news department.

This series is supported in part by Vertex, The Science of Possibility.

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Smarter health: Artificial intelligence and the future of American health care - WBUR News