August 24, 2023

3 data privacy principles to adopt now, even while governments still debate

Fairness is one of the most powerful guiding principles any brand can adopt for its use of data, but what does it mean in practice? On the one hand, it’s about considering how you’re using not just data but the tools and technologies that help you harness data in your marketing and decision-making. On the other hand, it’s important to remember we’re not just talking about one moment in time, like the moment when someone gives you their data, or the moment of an interaction between them and you, in a store or on your website. It’s about the potential implications that these moments can have down the line. Could it lead to an unfair, harmful, or discriminatory outcome for them? Could it keep them from getting credit? Or a job offer? Could it perpetuate a stereotype about a protected class of people? Building a foundation of fairness, for example, could mean implementing policies and procedures to regularly assess the data and tech you use to ensure they do not have a disparate impact on vulnerable consumers.

Cyber attackers using Gen AI more effectively than defenders

Both cyber attackers and defenders employ generative AI, but attackers use it more effectively. Adversaries capitalise on AI/ML, deepfake, facial recognition, and Augmented Reality/Virtual Reality (VR) (AR/VR) to enhance hacking strategies against government agencies, businesses, and strategic targets, surpassing cyber defenders in technological adaptation. Facial recognition and AR/VR systems illustrate the extensive use of deepfake technology by cybercriminals. We predict that within two years, social engineering and phishing attacks will predominantly employ deep fakes, making defenders' tasks much harder. Malware capabilities have evolved significantly. Instead of creating static malware, hackers now build multi-behavioural malware that adapts in real-time. Upon reaching a target, this malware assesses the environment and generates tailored malicious code, targeting various systems like Windows, Linux, Outlook, and mobile devices. This is powered by AI/ML engines, resulting in multi-behavioural, metamorphic, and polymorphic malware that dynamically alters their code as they spread.

Cloud Robotics: A New Frontier for Internet Technology

Robots connected to the cloud are being used in warehouses and distribution centers for material handling, order fulfillment, and inventory management duties. These robots are capable of independent navigation, object recognition and picking, and teamwork with human personnel. The medical sector is likewise ripe for transformation because to cloud robots. Robots connected to the cloud can access patient information, medical records, and cutting-edge disease-diagnosis algorithms. Cloud robotics alters how we connect with our domestic environment regarding home automation. Robots with cloud capabilities can automate harvesting, monitor crop health, and manage resource usage in agriculture. These robots can use the cloud to evaluate massive volumes of field data, forecast agricultural yields, and make quick judgments. Cloud robotics has tremendous promise as we look to the future. Advanced artificial intelligence (AI) and cloud robotics are being combined as a new trend, allowing robots to act more intelligently and quickly adapt to their surroundings.

Organizing Around Business Capabilities

A Value Structure is an idealized teaming structure illustrating how the organization delivers benefits to its customers. The idealized structure includes teams and roles to not only operate a capability, but also to build it. We call this structure the value structure to differentiate it from two other structures within an organization: formal structure and learning structure. The formal structure represents the way an organization structures its activities into jobs and job families, manages compensation and other aspects of human resources. The learning structure represents the way an organization learns to improve its performance, including role-based learning, team-based learning, and establishing a culture of relentless improvement without guilt or blame. Establishment of a value structure independent from formal and learning structures enables an organization to begin to change how it delivers value to customers without the overhead of changing formal reporting or job titles. The value structure makes impediments to the flow of value clearly visible so we can either eliminate them or explicitly orchestrate them.

How to Build True Cyber Resilience

Cyber resilience cannot be achieved by implementing one initiative or investing in one new technology. “CISOs should focus on the question, ‘How ready are we?’" says Hopkins. Are organizations ready to detect threats, respond to them, recover, and adapt to an ever-changing threat landscape? “The first step to building cyber resilience involves understanding which cyberattacks are most relevant to an organization based on its industry, location, IT ecosystem, data type, users, etc.,” says Tony Velleca, CISO at digital technology and IT service company UST and CEO of CyberProof, a UST security services company. Once an organization understands its risks, the question becomes how to detect those threats, stop them, and contain them if and when they become cybersecurity incidents. The answer lies in a blend of technology and talent. Combining the power of cybersecurity tools, such as zero trust and managed detection and response, can help organizations achieve cyber resilience, but they need to ensure the strategies they deploy make measurable progress toward that goal.

AI and the evolution of surveillance systems

AI models are influenced by the datasets used to train them. It is imperative that AI vendors carefully tune and balance their datasets to prevent biases from occurring. Balancing datasets is a manual process that requires making sure that the humans visible in the datasets are a good representation of reality, and do not have biases towards certain human traits. In our case, we use diverse groups of actors, from all over the world, to play out violence for our training datasets to ensure they are balanced. Furthermore, testing regularly for such biases can go a long way. A carefully designed system can protect and help people without significantly impacting their privacy. This requires considering privacy from designing to implementing AI systems. I believe that the future of AI-powered surveillance will see reduced privacy infringement. Currently, large surveillance installations still require humans looking at camera streams all the time. In a trigger-based workflow, where humans take actions after an AI has alerted them, the amount of security camera footage seen by humans is much less, and thus the risk of privacy infringement decreases.

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