Arnab’s expertise lies in providing solutions to complex problems in the area of IT Security. He has 23+ years of experience in leadership roles at large organizations like British Telecom, Tech Mahindra, iViZ (part of Synopsys), Metric Stream, Capgemini, IBM & more. Arnab was one of the key members to have worked in the BT21CN, one of the largest transformation projects in the telecom world aimed at the complete transformation of BT’s telecom network to Next Generation Network (NGN).
Agentic AI Security · Part 3 of 3 An agent can do something quite reasonable, with valid permissions, and still cause harm because of what happened before it or what another agent is doing at the same time. A runtime control reduces risk while an AI agent is acting, by checking each proposed operation against… Read More »The Most Dangerous Action May Be Perfectly Legitimate
Agentic AI Security · Part 2 of 3 Deterministic controls are essential when AI agents can act. They also have a precise limit: they can enforce what we have specified and can observe. The rest of the safety problem does not disappear. The FireCompass AI Safety and Governance series Your AI Agent Is Safe. Until… Read More »Guardrails Are Not a Safety Architecture
Agentic AI Security · Part 1 of 3 Model safety is not agent safety. Confusing the two may become one of the more consequential mistakes we make as AI moves from answering questions to taking actions. Agentic AI security is the practice of keeping an autonomous AI agent safe while it acts, not just while… Read More »Your AI Agent Is Safe. Until It Starts Acting.
Many objections to belief-state planning are framed as concerns about scalability. In practice, they are concerned about visibility. Systems that avoid explicit belief do not eliminate uncertainty; they merely conceal it. This concealment can appear efficient, but it comes at a cost that is paid later often at the point where decisions matter most. This… Read More »Combinatorial Belief States Are the Cost of Explicit Uncertainty
For most of the past decade, the trajectory of large language model research followed a familiar arc: scale up the compute, widen the data, tune the alignment, ship the product. Each new generation of models arrived with modestly improved benchmark scores, better instruction-following, and marginally reduced hallucination rates. Opus replaced Sonnet. Sonnet replaced Haiku. The… Read More »Demystifying Claude Mythos Preview: The Model That Changed Cybersecurity Forever