For all the complexity of modern software systems, some of the most devastating breaches still begin with something shockingly mundane: a hard-coded username, password, API token, or key hidden deep within a repository.
Hard-Coded Credentials: The Skeleton Key Hiding Inside Your Software Ecosystem Read More »
In our previous article, How Leaders Should Measure Success in AI-Driven Software Engineering, We argued that optimizing for the percentage of AI-generated code misses the point — what really matters is how fast and reliably organizations turn ideas into value. But saying “measure time-to-market” is easy. Doing it systematically is harder. Too often, teams chase a single KPI — like lead time or deployment frequency — and call it a success. The truth is that time-to-market isn’t a single number. It’s the result of balance: between how fast you deliver, how well you manage quality and risk, how efficiently work flows through your system, and how effectively you learn from customer feedback.
A Practical Framework for Measuring Success in AI-Driven Software Engineering Read More »
In the previous blog celebrating Cybersecurity Awareness Month, we explored how AI is transforming cybersecurity — empowering both attackers and defenders — and why trustworthy, transparent, and responsible AI adoption is essential for building resilient defenses.
From Awareness to AI Literacy: Enabling Human Oversight in the Age of Agentic AI Read More »
October marks Cybersecurity Awareness Month. The theme “Secure Our World” invites businesses (and individuals) to reflect on how digital risks are evolving and what it takes to stay ahead. It is an open invitation toward resilience in the face of attackers enhancing their playbooks by leveraging cutting-edge technologies and tactics.
AI in Cybersecurity: Striking the Balance Between Innovation and Trust Read More »
Imagine celebrating that 60% of your code is written by AI — only to realize your product still ships late.
That’s the trap many organizations are likely to fall into. Ambitious targets like “By 2026, half of our code will come from AI” sound bold, look measurable, and they can even make the case for strong boardroom slides.
How Leaders Should Measure Success in AI-Driven Software Engineering Read More »
From GitHub Copilot to ChatGPT plugins and IDE-integrated assistants, AI fundamentally changes how developers write and ship code. What once required hours of manual configuration and debugging can now be scaffolded in seconds by a smart prompt. But while productivity is skyrocketing, the rise of AI-assisted development introduces new challenges in code security, quality, and accountability.
Code, Comply, Repeat: Why Human Oversight is Essential in AI-Assisted Code Development Read More »
Computer Vision AI systems are much more complicated than AI systems using tabular datasets (typically extracted from databases) as the features they employ to make a decision (millions of pixels) are orders of magnitude more than what tabular datasets usually entail (tens to hundreds of database columns).
Is there an (easy) way of detecting bias and fairness in Computer Vision AI systems? Read More »
AI has shifted from experimental use cases to a core driver of digital transformation across industries. From AI-powered chatbots in financial services to predictive maintenance in manufacturing and fraud detection in retail, businesses are embedding AI into critical workflows.
From Guidance to Governance: Leading with OWASP’s AI Maturity Assessment Read More »
It’s already been a year since the EU AI Act’s operationalisation. We all need to realise that the Act has moved from policy to execution, and the next 12 months will separate the reactive from the ready (or proactive). Here is what’s coming next for businesses, citizens and organizations.
The Next Phase of the EU AI Act Is Here—Are You Leading or Lagging? Read More »
Introduction: A Community Contribution in the Era of AI Accountability
As organizations increasingly embed AI into critical workflows and products, a new challenge emerges: how do we know that these systems are actually doing what we intend—and doing it well?
More than just Accuracy: A Blueprint for Meaningful AI Evaluations Read More »
