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Organizations are rapidly adopting AI assistants, copilots, and agentic AI tools to improve productivity, automate workflows, summarize information, and interact with enterprise applications. These tools are powerful because they can access emails, calendars, documents, browsers, internal systems, APIs, and business data. That same power introduces a new risk: Promptware. Promptware is a form of prompt-driven malicious behaviour where an attacker uses text, documents, emails,

AI is changing how security teams think about threat modeling. Traditionally, threat modeling has depended heavily on manual review: architects document systems, security teams inspect data flows, reviewers identify risks, and risk owners decide what must be remediated or accepted. That work is valuable, but it can be slow, inconsistent, and difficult to scale across many applications, cloud environments, APIs, data flows, and third-party integrations. NorthernCiX has put for

AI is quickly moving from experimentation into core risk and security workflows. One area where this is especially powerful is threat modeling. An AI-assisted threat modeling platform can accelerate architecture review, identify potential threats, map evidence to risks, suggest controls, and support risk decisions. But this also creates a governance challenge: what happens when AI starts influencing how risks are identified, assessed, scored, and accepted? This is where the t

Artificial intelligence is quickly moving from passive tools to active digital agents. Traditional AI systems mainly responded to prompts, generated content, or helped users analyze information. Agentic AI goes further. These systems can perceive context, reason over goals, call tools, interact with APIs, move data, and even create sub-agents to complete tasks. That shift is powerful, but it also changes the cybersecurity conversation. When AI can act, it is no longer just a

As more organizations experiment with autonomous AI agents—systems that plan, reason, call tools, read/write memory, and execute real tasks—I keep noticing the same pattern: everyone is excited to deploy them, but very few are prepared to secure them. What surprises me most is not the complexity of the technology, but the gaps in our understanding of the risks . Most teams start with good intentions. They pull out familiar ML security frameworks, map out the architecture, an

As digital transformation accelerates across industries, governance frameworks have become essential for managing emerging technologies...

As cloud computing becomes foundational to modern enterprise IT infrastructure, assessing security strategies in cloud environments is...

Here are practical measures to ensure your organisation stays updated and compliant: 1. Comprehensive Governance and Policies Define and...

Here are key practical security measures to implement: 1. Robust Risk Management Effective risk management is paramount for AI systems,...

Here are some of the key potential risks associated with AI technologies: 1. Content and Output Risks Hallucinations are a critical...