From quarterly snapshots to continuous assurance: an agentic security advisor on Amazon Bedrock AgentCore   The customer A Thailand-based cloud and data-privacy consultancy that advises its own clients on data-protection compliance, and holds internationally recognised certifications in information security management and privacy management. AWS Partner: G-Able Public Company Limited   The challenge The customer needed continuous, evidence-backed assurance over its AWS security posture, and could not get it economically by hand. The requirement was sharper here than for a general enterprise: the company maintains certified management systems, so posture evidence is an obligation for its own surveillance audits — and it sells compliance services, so its credibility depends on its own posture being demonstrably sound. Manual review of one AWS account against the CIS AWS Foundations Benchmark — roughly 62 controls — costs 4 to 5 senior man-days per round, and frameworks make that recurring: PCI DSS requires assessment at least quarterly, while CIS Controls v8 points toward continuous monitoring that periodic review cannot achieve.   Four constraints followed: Cost of senior effort. For a consultancy, every man-day spent auditing its own estate is a man-day not delivered to a paying client. Detection latency. With quarterly assessment, a misconfiguration introduced just after a round can sit undetected for up to 90 days. Most cloud data exposure originates from customer-side misconfiguration, and Thai PDPA penalties reach THB 5 million per case. Evidence completeness. Manual review makes it hard to record consistently which controls were verified and which could not be reached — a distinction that matters when the output becomes audit evidence. Turnaround. Answering one posture question meant waiting for a senior engineer to gather the evidence by hand.     Why this needed an agentic solution “Is our data properly protected?” resolves into a chain — which buckets exist, how each is encrypted, which are public, what the key policies allow — and each answer changes the next question worth asking. A fixed rule engine cannot follow that chain, and a single model prompt has no access to live configuration.     The solution G-Able delivered Splendormon, its AWS Cloud Security Posture Management (CSPM) and IAM security platform, as a dedicated single-tenant installation in an AWS account dedicated to this customer. The installation and all of the customer’s data at rest run in the Asia Pacific (Thailand) Region. The Amazon Bedrock AgentCore layer runs in Asia Pacific (Singapore), where the […]

The customer A Thailand-based enterprise cybersecurity consulting and solutions provider. The company aligns its practice to the NIST Cybersecurity Framework and ISO/IEC 27001, has delivered more than 500 cybersecurity projects, and partners with global security vendors including Palo Alto Networks, CrowdStrike, Fortinet, Check Point, CyberArk, Splunk, Zscaler, SentinelOne, Imperva, Trellix and Devo. Roughly 90% of its staff are technical practitioners. AWS Partner: G-Able Public Company Limited   The challenge The customer’s presale function had become the constraint on revenue. Producing a proposal for an AWS-based cybersecurity engagement required a senior engineer to work manually through requirement analysis, inventory mapping, man-day effort sizing, pricing, proposal writing and solution design. That sequence consumed roughly five architect man-days per proposal, and the elapsed turnaround from brief to submitted proposal ran to 10.5 days. Three problems followed from that. Speed capped pipeline coverage. The number of opportunities the team could pursue in a quarter was limited by how fast proposals could be produced, not by how many opportunities existed. Sizing and pricing varied between engineers. Standards and rate assumptions lived in individual heads rather than in a governed system, so two engineers could size materially similar scopes differently. Manual pricing carried real financial risk. AWS list prices change. A figure assembled by hand against a stale price is either a loss absorbed at delivery or an uncomfortable conversation with the customer. Left unaddressed, the immediate exposure was lost opportunities to faster competitors and margin erosion on underpriced deals. The longer-term exposure was an inability to scale presale in step with demand — the same senior architects who write proposals are the ones who deliver billable work, so presale volume and delivery capacity compete directly for one scarce resource.   Why this needed an agentic solution Proposal production is not a single generative task. It is a sequence of dependent decisions: understand the requirement, select an architecture, size it, price it against live rates, write it, then route it for approval. Each step needs different knowledge and different tools, and a mistake early propagates silently to the end. A single large-language-model prompt cannot do this reliably, because the model has no way to look up a current AWS price and no structural reason to refuse to invent one. What the work needs is an agent that plans, calls real tools against real data, and is prevented by design from producing a number it   The […]

The supplier dashboard is a part of the wholesale company’s data monetization strategy. It provides insightful data analytics to help suppliers understand their own performance and capabilities, ensuring they maintain a successful and profitable business. The dashboard enhances suppliers’ visibility into their purchase and sales performance while also offering valuable insights into the overall performance of the wholesale company.