Data clean rooms are secure environments where multiple parties can analyze and share data without exposing raw data to each other. They enable collaborative analysis while ensuring privacy, compliance with data protection regulations, and strict access controls. These rooms use privacy-enhancing technologies to anonymize or aggregate data, allowing companies to gain insights from combined datasets without compromising individual privacy or proprietary information. The typical users include advertisers, publishers, data providers, and tech companies seeking to perform joint data analysis for marketing, research, and business intelligence.

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Which best describes your adoption of Privacy Enhancing Technologies (PETs) (e.g., synthetic data, clean rooms)?

Which best describes your adoption of Privacy Enhancing Technologies (PETs) (e.g., synthetic data, clean rooms)?

I spent about six years at Visa building data platforms and AI. On that journey, I saw a couple of problems. The volume, variety, and velocity of the kinds of alerts security operators look at was unmanageable. It took us about 3-5 years to get to a maturity level where you could say you got 30%-40% of MITRE ATT&amp;CK and Kill Chain coverage. Then the cloud happened, and the whole game changed again. For the most part, tools are built for on-prem and we are just getting started with cloud. Especially when it comes to cybersecurity operations and detection engineering: it's still manual, and people have to build the rules and models. So we're back to square one.


Now everybody is coming to the conclusion that perimeter is not the way to do things. So there can be a possibility of paradigm shift. Instead of going after the perimeter, we could focus on data. Understanding where your sensitive data lies and protecting that would make more sense. Strategically going after what you need to protect, as opposed to just going after everything.

From a simplistic perspective, at your public repositories where hard coded credentials and keys is the nirvana, I don't have to be intelligent. All I have to do is string searches. So those are some of the patterns you'll see from a government perspective. It's pretty diverse. 2000 was the first year Carnegie Mellon was tasked with secure software development. The government gave them $50 million to develop courses, so every university in the United States would start teaching secure software development. If you remember the 90s we had something called zero detect software. Clean rooms, clean software. We talked about how to avoid software errors. That was the concept and it introduced secure software development. It was never put into practice. 20 years later, we're saying everybody should focus on shift left: catch the errors early, before they become a problem. I'm glad to be going back to the fundamentals. And there's also now in the government a big push on actually vetting out code and also the libraries, the government is going to allow or not allow. So we have long ways to go as a country, I don't think we are anywhere close. But I'm pleased after 20 years, we're heading in the right direction. We're going back to the fundamentals and not a shiny object syndrome.

From a simplistic perspective, at your public repositories where hard coded credentials and keys is the nirvana, I don&#39;t have to be intelligent. All I have to do is string searches. So those are some of the patterns you&#39;ll see from a government perspective. It&#39;s pretty diverse. 2000 was the first year Carnegie Mellon was tasked with secure software development. The government gave them $50 million to develop courses, so every university in the United States would start teaching secure software development. If you remember the 90s we had something called zero detect software. Clean rooms, clean software. We talked about how to avoid software errors. That was the concept and it introduced secure software development. It was never put into practice. 20 years later, we&#39;re saying everybody should focus on shift left: catch the errors early, before they become a problem. I&#39;m glad to be going back to the fundamentals. And there&#39;s also now in the government a big push on actually vetting out code and also the libraries, the government is going to allow or not allow. So we have long ways to go as a country, I don&#39;t think we are anywhere close. But I&#39;m pleased after 20 years, we&#39;re heading in the right direction. We&#39;re going back to the fundamentals and not a shiny object syndrome.

Why aren’t today’s tools sufficient to prevent cyberattacks?

The right dataset is always the 98% to success. The more comprehensive, integral and noise-free the data is the less concerns you have with the model.

Bias and ethics … in both cases it comes down to humans applying the technology appropriately and ensuring the training data is appropriate:

The degree of intelligence in AI comes with quality of data used for training and its very rare to get perfect dataset in any field. So, the bais introduced due to this can lead to different results leading to difficult real life situations. So, applications of AI into fields that deal with human lives, economy, physical security, privacy like healthcare, banking, threat scanners etc. can be very concerning. The interpretation of a situation to get right context and handling within moral/ethical domain is still a big challenge with using AI.

Primarily two things - 1) AI is built on probabilistic models that can be altered through the learning process that can be altered via data sets that can be modified. This means someone with bad intent can misuse it. 2) AI cannot think. It can simply process information that can result in biased results. Human life is not black or white. Our decisions are not black or white.

The most concerns about AI are related to:

Determining the right data set, Data security and storage, Infrastructure and Integration

I would be concerned about the completeness of the results. The results of AI depend on a set of data available at the moment. How good was the data at that moment the analysis was run? It's going to be tough job to keep it up with for AI engineers. Learning curve and employee retention is going to be challenging. In some industries e.g. Cybersecurity, data feeds change every second, the results of AI would change fast, how complete is the analysis or result of the algorithms!!

I don't have any strong concerns. Like anything, if you have bad data then you won't get meaningful results - the data issue is less of an AI problem and more just are you able to frame the problem properly and are you able to get the right (accurate) data to feed into the models for your problem.

Much the same that is concerning about other big data - who controls the data, what bias does it have, and who is ensuring the security of any type system?

AI will revolutionize the future in many sectors, the critical part of AI is to have good clean data for AI modeling, the risk is about available skill sets and validating the AI output to meet the organization requirements.

1. Companies utilising algorithms and data without understanding / having been disclosed the underlying logic and data sources --&gt; data cards will help this in the future
2. The post-hoc application of ethical considerations, instead of asking the question upfront "what are the potential consequences and should be really be doing this" --&gt; for a good discussion on this topic check out the various resources by ISO, IEEE and the OECD
3. Notwithstanding my concerns about 1. and 2. the speed and lack of understanding that underpins some of the legislation and regulation in the US, Europe and other parts of the world potentially create more problems that they solve. A decent example of how this could work can be found in the approach by the Singapore government.

I am concerned about the potential for AI to be used for malicious purposes, such as creating false information or manipulating data to achieve a desired outcome. I am also concerned about the potential for AI to be used to invade our privacy, for example through the use of facial recognition technology.

The biggest concern I have is bias in the training data. We've seen it happen on several products and the impacts can be significant, both financially and morally.

What concerns me the most about AI isn't actually necessarily about AI itself, but about the fear of humans/businesses making decisions solely using AI as the 'source' or 'deciding factor' without vetting which variables went in to the data that AI was using in the first place.

Data Quality. Users of AI technology are seemingly oblivious, or at least less aware than they should be, that behind a good AI solution needs to be quality data. The focus on any AI solution as a product should reference back to the quality of the data and data products behind it as a visible 'flag' to support trust and accuracy on the result. Perhaps a slightly naive view when you consider the nature of AI in also 'doing this itself', but I would argue at least for now, at the beginning, a key foundational dimension of 'getting it right'.

Don't get me started about AI.
Security and breach issues, [probably] unrealistic data and search results... and the list goes on.
As many people have already stated, it can be easily weaponized without even the vendor knowledge.
AI is a very touchy and sensitive issue, it may seem like a long shot for some, but, at least to me, I consider it like the atomic bomb when it was first created. By that I mean, who knows where or when the AI-Hroshima/Nagasaki "bombing" will occur?
I will stop here...

People not knowing how to use to properly, resulting in data security issues, privacy issues, biased decision making, non-ethical usage without limits

Data that is used to direct users to the interests of the creator or company that controls the AI.

Alongside its benefits, AI brings several concerns that must be addressed. 

Ethical considerations around potential reinforcement of biases, privacy issues due to data misuse, job displacement due to automation, over-dependence that might diminish human skills, and opaque decision-making mechanisms are some of these challenges. 

While AI can transform various facets of society, these concerns necessitate prudent regulation and ethical guidelines. Ensuring transparency, promoting responsible use, and fostering a balanced human-AI coexistence is paramount as we continue to integrate AI more deeply into our lives. 

We must manage these concerns responsibly to realize AI's full potential.

Overall what concerns me most about AI, and call me old fashioned ;-), is the impact on human interaction. YES 1) it will be a great enabler to work more efficient, benefit from data, predictive analytics etc so the avoid that people are to become masters in Excel or other applications if AI can generate data 24/7 and when you need. 2) On the other side, I know I am not the only one, you already being bombarded with emails from companies that use bots and where it is clearly a one size fits all to poach for business, to invite you to webinars, to pass on offerings on tools and applications and it gets annoying as we seem to go back to cold calling and chasing you based on AI generated data from LinkedIn or others that you signed up for and where clearly your data has been sold to an AI tool to contact you. 3) for candidates the process can be automated and digitalized and a bot or AI can manage the entire process for you but you still want to sense the culture, values and ethics by meeting future colleagues and peers. The bot as well as AI can do the sell and drive the process, they can't sell you the WHY you should join the company. So, yes as an enabler to and to create efficiency but not to replace interaction and genuine interest. It is probably going in that direction but social and communication skills are equally important than analytics and data .

My biggest concern: top industry leaders pandering to government officials, publicly asking for regulation, knowing it is impossible in practice, and doing so to impede competition while pretending to be altruistic.  Even if our government were to impose regulations (despite being completely unqualified to do so) the odds that they would be effective in practice are zero.  Driving the work underground, harming our own national interests by impeding progress, and watching as other countries move ahead without a boat anchor dragging behind them would be entirely counterproductive.  Tech industry execs know all of this but talk of a "six month pause" and government regulation anyway.  You cannot stop progress.  At best you can slow it down and put our nation at a competitive disadvantage, drive away the top talent, and help our adversaries in the process.  Note that we were not even able to prevent North Korea from developing atomic weapons despite massive efforts.  Building AI with open-source software and commercially available GPUs is 1000x easier than building an atomic bomb.  Get the regulators and politicians out of the room, let the industry and academia lead, and accept that it will be a bumpy ride.  There is no viable alternative.  Regulation will do more harm than good.

My biggest concern related to artificial intelligence is data privacy and ethics. The question is to what extent these two aspects will be respected and taken into consideration for the benefit of everyone.

Increased complexity in architectural ecosystem design and management to efficiently adapt to new sources, data management requirements, and data engineering for model monitoring and continuous optimisation.

Lack of functional implementation of responsible AI framework in a way that support key organisational stakeholders alike. A comprehensive governance model is required to bridge the gap between the principles and practice by AI practitioners within organisations. I propose a layered approach in this regard, with useful recommendations that organisations should consider to achieve integration between the layers of the principles and practical AI processes execution. This
also naturally spotlight the responsibility of the organisational stakeholders and collaboration required, by providing a practical view of the different requirements of the actions and
leverage over AI systems, and decision-making horizons. The ultimate outcome of this design is a concrete governed entity of a socio-technical system that consists of people, organisational process, work systems, data, and related ecosystem institutions. I propose four layers- societal, industry, organisational, Internal AI system layers respectively.

This must all be measured via a Responsible AI scorecard covering the following elements:

1. AI system and design
2. Algorithms 
3. Data Management &amp; Operations 
4. Risk &amp; Impact
5. Transparency, Explainability &amp; Contestability 
6. Accountability &amp; Ownership 
7. Development &amp; Technology Operations 
8. Compliance

This then dovetails into an elevated report for various board subcommittees based on elements- these committee are often the IT &amp; Data committee for the technical elements, Social &amp; ethics committee and Audit &amp; Risk Committee for the risk &amp; impact, accountability, transparency and compliance issues.

Best Data Clean Rooms Reviews 2024 | Gartner Peer Insights

appsflyer helps to trackuser data for app events which help in targeting intentful audience in marketing and improving consideration and purchase funnel

I have been using AppsFlyer to track, analyse and segment user data for over 3 years now. AppsFlyer helps in easily tracking the user activity and segmenting user databases for remarketing purposes. AppsFlyer also helps with elimination fraud with the help of P360

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Acxiom has greatly improved our data management by integrating different data sources with great accuracy. Its strong security features are quite reassuring to the protection of sensitive information, while analytics can still be performed in detail. It is a real dependable workhorse in support of our security operations.

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LiveRamp Clean Room

Snowflake AI Data Cloud is a data warehouse with many features which can solve problems related to data, it is one point solution in the data cloud which uses AI capabilities to resolve the issues, it provides time travel features to deal with data loss.

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