September 05, 2024

What Does the Car of the Future Look Like?

Enabled with IoT, the vehicles stay in sync with their environments. The ConnectedDrive feature, for example, enables predictive maintenance by using IoT sensors to monitor vehicle health and performance in real time and notify drivers about upcoming maintenance needs. IoT also paves the way for vehicle-to-infrastructure, or V2X, communication, which enables BMW cars to interact with traffic lights, road signs and other vehicles. But a smart car is more than just internet-connected. ... The next leap in sensor technology is quantum sensing. Image generation systems based on infrared, ultrasound and radar are already in use. But with multisensory systems, BMW vehicles will not only be able to detect potential hazards more accurately but also predict and prevent damage - a capability crucial for automated and autonomous driving systems. These sensors will allow vehicles to "feel" their surroundings, enabling more refined surface control and the ability to perform complex tasks, such as the automated assembly of intricate components. Predictive maintenance, powered by multisensory input, will serve as an early warning system in production, reducing downtime.

NIST Cybersecurity Framework (CSF) and CTEM – Better Together

CSF's core functions align well with the CTEM approach, which involves identifying and prioritizing threats, assessing the organization's vulnerability to those threats, and continuously monitoring for signs of compromise. Adopting CTEM empowers cybersecurity leaders to significantly mature their organization's NIST CSF compliance. Prior to CTEM, periodic vulnerability assessments and penetration testing to find and fix vulnerabilities was considered the gold standard for threat exposure management. The problem was, of course, that these methods only offered a snapshot of security posture – one that was often outdated before it was even analyzed. CTEM has come to change all this. The program delineates how to achieve continuous insights into the organizational attack surface, proactively identifying and mitigating vulnerabilities and exposures before attackers exploit them. To make this happen, CTEM programs integrate advanced tech like exposure assessment, security validation, automated security validation, attack surface management, and risk prioritization.

Leveling Up to Responsible AI Through Simulations

This simulation highlighted the challenges and opportunities involved in embedding responsible AI practices within Agile development environments. The lessons learned from this exercise are clear: expertise, while essential, must be balanced with cross-disciplinary collaboration; incentives need to be aligned with ethical outcomes; and effective communication and documentation are crucial for ensuring accountability. Moving forward, organizations must prioritize the development of frameworks and cultures that support responsible AI. This includes creating opportunities for ongoing education and reflection, fostering environments where diverse perspectives are valued, and ensuring that all stakeholders—from engineers to policymakers—are equipped and incentivized to navigate the complexities of responsible Agile AI development. Simulations like the one we conducted are a valuable tool in this effort. By providing a realistic, immersive experience, they help professionals from diverse backgrounds understand the challenges of responsible AI development and prepare them to meet these challenges in their own work. As AI continues to evolve and become increasingly integrated into our lives, the need for responsible development practices will only grow.

What software supply chain security really means

Upon reflection, the “supply chain” aspect of software supply chain security suggests the crucial ingredient of an improved definition. Software producers, like manufacturers, have a supply chain. And software producers, like manufacturers, require inputs and then perform a manufacturing process to build a finished product. In other words, a software producer uses components, developed by third parties and themselves, and technologies to write, build, and distribute software. A vulnerability or compromise of this chain, whether done via malicious code or via the exploitation of an unintentional vulnerability, is what defines software supply chain security. I should mention that a similar, rival data set maintained by the Atlantic Council uses this broader definition.?I admit to still having one general reservation about this definition: It can feel like software supply chain security subsumes all of software security, especially the sub-discipline often called application security. When a developer writes a buffer overflow in the open source software library your application depends upon, is that application security? Yep! Is that also software supply chain security?

Data privacy and security in AI-driven testing

As the technology has become more accepted and widespread, the focus has shifted from disbelief in its capabilities to a deep concern for how it handles sensitive data. At Typemock, we’ve adapted to this shift by ensuring that our AI-driven tools not only deliver powerful testing capabilities but also prioritize data security at every level. ... While concerns about IP leakage and data permanence are significant today, there is a growing shift in how people perceive data sharing. Just as people now share everything online, often too loosely in my opinion, there is a gradual acceptance of data sharing in AI-driven contexts, provided it is done securely and transparently.Greater Awareness and Education: In the future, as people become more educated about the risks and benefits of AI, the fear surrounding data privacy may diminish. However, this will also require continued advancements in AI security measures to maintain trust. Innovative Security Solutions: The evolution of AI technology will likely bring new security solutions that can better address concerns about data permanence and IP leakage. These solutions will help balance the benefits of AI-driven testing with the need for robust data protection.

QA's Dead: Where Do We Go From Here?

Developers are now the first line of quality control. This is possible through two initiatives. First, iterative development. Agile methodologies mean teams now work in short sprints, delivering functional software more frequently. This allows for continuous testing and feedback, catching issues earlier in the process. It also means that quality is no longer a final checkpoint but an ongoing consideration throughout the development cycle. Second, tooling. Automated testing frameworks, CI/CD pipelines, and code quality tools have allowed developers to take on more quality control responsibilities without risking burnout. These tools allow for instant feedback on code quality, automated testing on every commit, and integration of quality checks into the development workflow. ... The first opportunity is down the stack, moving into more technical roles. QA professionals can leverage their quality-focused mindset to become automation specialists or DevOps engineers. Their expertise in thorough testing can be crucial in developing robust, reliable automated test suites. The concept that "flaky tests are worse than no tests" becomes even more critical when the tests are all that stop an organization from shipping low-quality code.

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