Category Archives: artificial intelligence

Top Pros and Cons of Disruptive Artificial Intelligence (AI) in InfoSec

Fig. 1. Swenson, Jeremy, Stock; AI and InfoSec Trade-offs. 2024.

Disruptive technology refers to innovations or advancements that significantly alter the existing market landscape by displacing established technologies, products, or services, often leading to the transformation of entire industries. These innovations introduce novel approaches, functionalities, or business models that challenge traditional practices, creating a substantial impact on how businesses operate (ChatGPT, 2024). Disruptive technologies typically emerge rapidly, offering unique solutions that are more efficient, cost-effective, or user-friendly than their predecessors.

The disruptive nature of these technologies often leads to a shift in market dynamics, digital cameras or smartphones for example. These with new entrants or previously marginalized players gain prominence while established entities may face challenges in adapting to the transformative changes (ChatGPT, 2024). Examples of disruptive technologies include the advent of the internet, mobile technology, and artificial intelligence (AI), each reshaping industries and societal norms. Here are four of the leading AI tools:

1.       OpenAI’s GPT:

OpenAI’s GPT (Generative Pre-trained Transformer) models, including GPT-3 and GPT-2, are predecessors to ChatGPT. These models are known for their large-scale language understanding and generation capabilities. GPT-3, in particular, is one of the most advanced language models, featuring 175 billion parameters.

2.       Microsoft’s DialoGPT:

DialoGPT is a conversational AI model developed by Microsoft. It is an extension of the GPT architecture but fine-tuned specifically for engaging in multi-turn conversations. DialoGPT exhibits improved dialogue coherence and contextual understanding, making it a competitor in the chatbot space.

3.       Facebook’s BlenderBot:

BlenderBot is a conversational AI model developed by Facebook. It aims to address the challenges of maintaining coherent and contextually relevant conversations. BlenderBot is trained using a diverse range of conversations and exhibits improved performance in generating human-like responses in chat-based interactions.

4.       Rasa:

Rasa is an open-source conversational AI platform that focuses on building chatbots and voice assistants. Unlike some other models that are pre-trained on large datasets, Rasa allows developers to train models specific to their use cases and customize the behavior of the chatbot. It is known for its flexibility and control over the conversation flow.

Here is a list of the pros and cons of AI-based infosec capabilities.

Pros of AI in InfoSec:

1. Improved Threat Detection:

AI enables quicker and more accurate detection of cybersecurity threats by analyzing vast amounts of data in real-time and identifying patterns indicative of malicious activities. Security orchestration, automation, and response (SOAR) platforms leverage AI to analyze and respond to security incidents, allowing security teams to automate routine tasks and respond more rapidly to emerging threats. Microsoft Sentinel, Rapid7 InsightConnect, and FortiSOAR are just a few of the current examples

2. Behavioral Analysis:

AI can perform behavioral analysis to identify anomalies in user behavior or network activities, helping detect insider threats or sophisticated attacks that may go unnoticed by traditional security measures. Behavioral biometrics, such as analyzing typing patterns, mouse movements and ram usage, can add an extra layer of security by recognizing the unique behavior of legitimate users. Systems that use AI to analyze user behavior can detect and flag suspicious activity, such as an unauthorized user attempting to access an account or escalate a privilege.

3. Enhanced Phishing Detection:

AI algorithms can analyze email patterns and content to identify and block phishing attempts more effectively, reducing the likelihood of successful social engineering attacks.

4. Automation of Routine Tasks:

AI can automate repetitive and routine tasks, allowing cybersecurity professionals to focus on more complex issues. This helps enhance efficiency and reduces the risk of human error.

5. Adaptive Defense Systems:

AI-powered security systems can adapt to evolving threats by continuously learning and updating their defense mechanisms. This adaptability is crucial in the dynamic landscape of cybersecurity.

6. Quick Response to Incidents:

AI facilitates rapid response to security incidents by providing real-time analysis and alerts. This speed is essential in preventing or mitigating the impact of cyberattacks.

Cons of AI in InfoSec:

1. Sophistication of Attacks:

As AI is integrated into cybersecurity defenses, attackers may also leverage AI to create more sophisticated and adaptive threats, leading to a continuous escalation in the complexity of cyberattacks.

2. Ethical Concerns:

The use of AI in cybersecurity raises ethical considerations, such as privacy issues, potential misuse of AI for surveillance, and the need for transparency in how AI systems operate.

3. Cost and Resource Intensive:

Implementing and maintaining AI-powered security systems can be resource-intensive, both in terms of financial investment and skilled personnel required for development, implementation, and ongoing management.

4. False Positives and Negatives:

AI systems are not infallible and may produce false positives (incorrectly flagging normal behavior as malicious) or false negatives (failing to detect actual threats). This poses challenges in maintaining a balance between security and user convenience.

5. Lack of Human Understanding:

AI lacks contextual understanding and human intuition, which may result in misinterpretation of certain situations or the inability to recognize subtle indicators of a potential threat. This is where QA and governance come in case something goes wrong.

6. Dependency on Training Data:

AI models rely on training data, and if the data used is biased or incomplete, it can lead to biased or inaccurate outcomes. Ensuring diverse and representative training data is crucial to the effectiveness of AI in InfoSec.

About the author:

Jeremy Swenson is a disruptive-thinking security entrepreneur, futurist / researcher, and senior management tech risk consultant. He is a frequent speaker, published writer, podcaster, and even does some pro bono consulting in these areas. He holds an MBA from St. Mary’s University of MN, an MSST (Master of Science in Security Technologies) degree from the University of Minnesota, and a BA in political science from the University of Wisconsin Eau Claire. He is an alum of the Federal Reserve Secure Payment Task Force, the Crystal, Robbinsdale and New Hope Citizens Police Academy, and the Minneapolis FBI Citizens Academy.

No Interview Needed to Join Microsoft After Getting Fired From OpenAI – Sam Altman

Fig. 1. Former OpenAI CEO Sam Altman and Microsoft CEO Satya Nadella. Getty Images, 2023.

#chatGPT #Microsoft #openai #boardgovernance

Update: Sam Altman is returning to OpenAI as CEO, ending days of drama and negotiations with the help of heavy investor Microsoft and Silicon Valley insiders (Bloomberg, 11/22/23). In sum, there were more issues without Sam than with him and the board realized that pretty fast. So now some board members have to be shown the door.

Some may view a fired executive like Sam Altman as damaged goods but we all know that corporate boards get these things wrong all the time, and it’s more about office politics and cliques than substantive performance.

The board described their decision as a “deliberative review process which concluded that he was not consistently candid in his communications with the board, hindering its ability to exercise its responsibilities. The board no longer has confidence in his ability to continue leading OpenAI.” Yet the board’s statement makes little sense and is out of context for an emerging technology at a time such as this.

As a result of this nonsensical firing, there was likely no job interview when Sam Altman joined Microsoft. He was already validated as a thought leader in the tech and generative AI community, so it was hardly needed. Microsoft CEO Satya Nadella was a fan and already invested billions into OpenAI. He saw the open opportunity and took it fast before another tech company could. The same thing happened when Oracle CEO Larry Ellison hired Mark Hurd in 2010 after HP fired him and the results were great.

This begs the question of how valuable are job interviews in the area of emerging tech or for people with visible achievements. What is the H.R. screener or some tech director in a fiefdom going to ask you? They would hardly understand the likely answers in a meaningful way anyway. I know many tech and business leaders who have wasted time in dumb interviews in contexts such as these and it is a poor reflection of the companies setting them up this way.

In other words, plenty of people will not want to work for OpenAI because of how Altman was publicly treated while Microsoft looks more inclusive and forward-thinking. So I am sure many people will leave OpenAI to follow Altman at Microsoft and that is really how OpenAI shot themselves in the foot especially considering Microsoft’s size.

Any failings and risks designed into ChatGPT are as much the problem of OpenAI as they are for every other company working in this vastly unknown and emerging area of tech. To blame that on Altman in this context seems unreasonable and thus he is a fall guy.

There are good and bad things with AI just like with any technology, yet the good far outweighs the bad in this context. Microsoft knows that there are problems in AI in cyber security, fraud, IP theft, and more. The bigger and more capable their AI team the better they can address these issues, now with Altman’s help.

Now, of course, Altman has to be evaluated on his performance at Microsoft making sure AI stays viable and within the approved guardrails, and hopefully innovates a few solutions to make society better. Yet the free market of other tech companies and regulators also have that responsibility.

About the Author:

Jeremy Swenson is a disruptive-thinking security entrepreneur, futurist/researcher, and senior management tech risk consultant. Over 17 years he has held progressive roles at many banks, insurance companies, retailers, healthcare orgs, and even governments including being a member of the Federal Reserve Secure Payment Task Force. Organizations relish in his ability to bridge gaps and flesh out hidden risk management solutions while at the same time improving processes. He is a frequent speaker, published writer, podcaster, and even does some pro bono consulting in these areas. As a futurist, his writings on digital currency, the Target data breach, and Google combining Google + video chat with Google Hangouts video chat have been validated by many. He holds an MBA from St. Mary’s University of MN, an MSST (Master of Science in Security Technologies) degree from the University of Minnesota, and a BA in political science from the University of Wisconsin Eau Claire.

Three Unique Tech Trends in 2017 and Implications for 2018

Minneapolis – 12/24/2017

Each year we like to review and commentate on the most impactful technology and business concepts that are likely to significantly impact the coming year. Although this list is incomplete, these are three items worth dissecting.

3. The Hyper Expansion of Cloud Services Will Spur Competition and Innovation:
Cloud computing is a utility that relies on shared resources to achieve a coherent economy of scales benefit – with high-powered services that are rapidly provisioned with minimal management effort via the internet (Fig. 1). It presently consists of these main areas: SaaS (software as a service), PaaS (platform as a service), and IaaS (infrastructure as a service). It is typically used for technology tool diversification, redundancy, disaster recovery, storage, cost reduction, high powered computer tests and models, and even as a globalization strategy. Cloud computing generated about $127 billion in 2017 and is projected to hit $500 billion by the year 2020. At this rate, we can expect many more product startups and consulting services firms to grow and consolidate in 2018 as they are forced to be more competitive thus bringing costs down.

The line between local and cloud computing is blurry because the cloud is part of almost all computer functions. Consumer-facing examples include: Microsoft OneDrive, Google Drive, GMAIL, and the iPhone infrastructure. Apple’s cloud services are primarily used for online storage, backups and synchronization of your mail, calendar, and contacts – all the data is available on iOS, Mac OS, and even on Windows devices via the iCloud control panel.

Fig. 1. Linked Use Cases for Cloud Computing.
Cloud Infra

More business sided examples include: Salesforce, SAP, IBM CRM, Oracle, Workday, VMware, Service Now, and Amazon Web Services. Amazon Cloud Drive offers storage for music, images purchased through Amazon Prime, as well as corporate level storages that extends services for anything digital. Amazon’s widespread adoption of hardware virtualization, service-oriented architecture with automated utilization will sustain the growth of cloud computing. With the cloud, companies of all sizes can get their applications up and running faster with less IT management involved and with much lower costs. Thus, they can focus on their core-business and market competition.

The big question for 2018 is what new services and twists will cloud computing offer the market and how will it change our lives. In tackling this question, we should try to imagine the unimaginable. Perhaps in 2018 the cloud will be the platform where combined supercomputers can use quantum computing and machine learning to make key breakthroughs in aerospace engineering and medical science.  Additionally, virtual reality as a service sounds like the next big thing; we will coin it (VRAAS).

2. The Reversal of Net Neutrality is Awful for Privacy, Democracy, and Economics:
Before it was rolled back, net neutrality required service providers to treat all internet traffic equally. This is morally and logically correct because a free and open internet is just as important as freedom of the press, freedom of speech, and the free market concept. The internet should be able to enable startups, big companies, opposing media outlets, and legitimate governments in the same way and without favor. The internet is like air to all these sects of the economy and to the world.

Rolling back net neutrality is something the U.S. will regret in coming months. Although the implications of it are not fully known, it may mean that fewer data centers will be built in the U.S. and it may mean that smaller companies will be bullied out of business due to gamified imbalances of cost in internet bandwidth. Netflix and most tech companies dissented via social media resulting in viral support (Fig 2).

Fig 2. Viral Netflix Opposition to Rolling Back Net Neutrality.
Netflix Twitter

Lastly, it exacerbates the gap between the rich and the poor and it enables the government to have a stronger hand in influencing the tenor of news media, social norms, and worst of all political bias. As fiber optic internet connectivity expands, and innovative companies like Google, Twitter, and Facebook turn into hybrid news sources, a fully free internet is the best thing to expose their own excesses, biases, and that there are legitimate conflicting viewpoints that can be easily found.

1. Amazon’s Purchase of Whole Foods Tells Us the Gap Between Retailer and Tech Service Company is Closing:

For quite a long time I have been a fan of Amazon because they were anti-retail establishment. In fact, in Amazon’s early days, it was the retail establishment that laughed at them suggesting they would flounder and fail. “How dare you sell used books by mail out of a garage”. Yet their business model has turned more into a technology and logistics platform than a product-oriented one. Many large and small retailers and companies of all types – employ their selling, shipping, and infrastructure platform to the degree that they are, in essence, married to Amazon.

Magazine Business Insider said, “The most important deal of the year was Amazon’s $13.7 billion-dollar acquisition of Whole Foods. In one swoop, Amazon totally disrupted groceries, retail delivery, and even the enterprise IT market” (Weinberger, 12/17/17). The basis for this acquisition was that grocery delivery is underserved and has huge potential in the U.S. as the population grows, less people own cars, and people value not wasting time walking around a retail store so much (getting socialized to a new level of service) (Fig 3).

Fig. 3. How Amazon Can Use Whole Foods to Serve High Potential Grocery Delivery.
Amazon Whole Foods

By Jeremy Swenson and Angish Mebrahtu

Mr. Swenson and Mr. Mebrahtu meet in graduate business school where they collaborated on global business projects concerning leadership, team dynamics, and strategic innovation. They have had many consulting stints at leading technology companies and presently work together indirectly at Optum / UHG. Mr. Swenson is a Sr. consultant, writer, and speaker in: business analysis, project management, cyber-security, process improvement, leadership, and abstract thinking.  Mr. Mebrahtu is a Sr. developer, database consultant, agile specialist, application design and test consultant, and Sr. quality manager of database development.

 

 

Jeremy Swenson
Angish Mebrahtu

 

Three Points on Artificial Intelligence and Cyber-Security for 2017

icit-new-logo-for-website5
Although I have been known for longer posts, I would like to offer only three things to watch out for related to artificial intelligence and cyber-security for 2017, followed by sharing two videos.

1) Cyber attackers have long used machine learning and automation techniques to streamline their operations and may soon use full-blown artificial intelligence to do it. Botnets will become self-healing and will be able to detect when they are being discovered and can re-route in response. The botnet and cyber crime business will grow and become more organized. Showdan, the world’s first search engine for internet connected devices, will be used to target companies and individuals negatively. Yet it can also be used for safety and compliance monitoring, most likely when its feed into another analytical tool.

How to Hack with Showdan (For Educational Purposes Only):

2) It won’t be long until A.I. learns the patterns of mutating viruses and then has the ability to predict and/or stop them in their tracks. This is dependent on the most up to date virus definitions, and corresponding algorithms. How a Zero Day is made is heavily a math problem applied to a certain context and operating system. There should be a math formula to predict the next most likely Zero Day exploit – A.I. could provide this. It’s a matter of calculating all possible code various and code add on variations. It’s a lot more advanced than a Rubix Cube.
975f495fafd8c494591892412ecf87e33) A.I. has the potential to close the gap between the lesser developed world and the developed world. The technology behind A.I. is not limited to big companies like IBM or Microsoft for the long term. We may be surprised with tech start-ups out of the lesser developed world who are very creative. Lack of fiber optic cable connectivity has forced many lesser developed nations to rely heavily on cell tower smartphone based internet communications. This has inspired a mobile app growth wave in parts of Africa as described here; “the use of smartphones and tablets within the country has led to a mobile revolution in Nigeria. Essentially, people now tend to seek mobile solutions more often and thus, enhance the growth of the mobile app development industry” (Top 4 Mobile App development companies in Nigeria, IT News Africa, 2015). A.I. will likely close the gap between these two sectors though not drastically change it. If lesser developed countries can build their own mobile apps and outsource things to A.I.; they could become more independent from the economic constraints of the developed world.

The below video highlights some of the complications around these points. It is from a conference hosted by the ICIT on April 25, 2016, and I did not attend this. In the video, Donna Dodson (Associate Director, Chief Cybersecurity Advisor and Director, NIST), Mark Kneidinger (Director, Federal Network Resiliency, DHS), Malcolm Harkins (ICIT Fellow – Cylance) and Stan Wisseman (ICIT Fellow – HPE) discuss related concepts and share realistic examples of how these technologies are reshaping the cyber-security landscape.

ICIT Forum 2016: Artificial Intelligence Enabling Next-Generation Cybersecurity

If you want to contact me to discuss these concepts click here.