Secure Access Service Edge and Zero Trust Network Access change what remote-access telemetry means to a security analyst. Traditional VPN architecture often places the user “inside” a broad network after authentication. ZTNA is designed to grant access to specific applications or resources based on identity, device, policy, and context, while SASE combines network and security functions through a cloud-delivered architecture that can include ZTNA, secure web gateway, cloud access security broker, firewall services, and SD-WAN. CISA and partner agencies have urged organizations to move toward modern approaches such as Zero…

Post-quantum readiness is the work required to move systems away from public-key cryptography that future cryptographically relevant quantum computers could break. Security teams do not need to predict the exact date such a machine will exist before beginning. NIST states that migration should start now, and its finalized post-quantum standards are ready to implement. The operational problem is larger than replacing an algorithm name. Organizations first need cryptographic visibility: where RSA and elliptic-curve cryptography are used, which certificates and protocols depend on them, which vendors control the implementation, how long…

Detection engineering is the discipline of turning an adversary hypothesis into a reliable operational signal. A rule is only one implementation artifact. Mature detection engineering starts with the behavior to detect, identifies the telemetry that can prove it, writes analytics that survive normal variation, tests them against benign and malicious examples, tunes the resulting alerts, and measures whether the signal helps analysts make a better decision. MITRE ATT&CK’s current framework has moved toward detection strategies and platform-specific analytics rather than treating high-level data-source labels as the endpoint. ATT&CK v18 deprecated…

AI security changes the defender’s problem in two directions. Security teams must protect AI systems as new attack surfaces, and they must prepare for adversaries who use AI to make phishing, reconnaissance, malware development, influence, fraud, and automation more scalable. The important distinction is that AI does not replace familiar cybersecurity fundamentals. It changes the speed, volume, interface, and failure modes around identity, data, software, and decision systems. NIST’s current Cyber AI Profile work organizes the problem into securing AI system components, using AI for cyber defense, and thwarting AI-enabled…

Vector search in Amazon Bedrock RAG converts a user query and source content into embeddings so semantically similar chunks can be retrieved even when they do not share the same exact words. Bedrock Knowledge Bases can connect to several supported vector stores and can quick-create some of them. Current options include Amazon OpenSearch Serverless, Aurora PostgreSQL Serverless, Neptune Analytics, Amazon S3 Vectors, and other supported stores depending on Region and knowledge-base configuration. The vector engine is only one part of retrieval quality. Embedding model, dimensions, chunking, metadata, filter eligibility, search…

Troubleshooting Amazon Bedrock applications is easier when the system is decomposed into layers: client and API edge, IAM, Bedrock runtime, model behavior, retrieval, agent orchestration, tool execution, networking, and downstream data. A single user-visible symptom such as “the answer failed” can be caused by HTTP validation, missing model permission, throttling, stale knowledge, tool errors, or simply a model that produced an unhelpful response. AWS exposes several evidence sources for Bedrock operations. The runtime publishes CloudWatch metrics for invocation volume, latency, token use, and errors. CloudTrail records Bedrock API activity according…

Testing a generative AI application on AWS requires evidence at several layers: deterministic application logic, prompt and model behavior, retrieval quality, agent tool use, safety, latency, cost, IAM boundaries, and production observability. A green unit-test suite is necessary but insufficient when model output is probabilistic and the application can call external tools or retrieve mutable knowledge. Amazon Bedrock provides model and RAG evaluation capabilities, while Amazon Bedrock AgentCore Evaluations now provides dedicated agent evaluation. AWS release notes state that AgentCore Evaluations became generally available in March 2026, with built-in evaluators,…

Amazon Bedrock can be accessed through interface VPC endpoints powered by AWS PrivateLink. That gives workloads inside a VPC a private path to Bedrock control-plane, runtime, Agents build-time, Agents runtime, and related supported endpoints without depending on an internet gateway, NAT gateway, Site-to-Site VPN, or Direct Connect simply to reach the service. Private connectivity can reduce public exposure and data-egress paths, but it does not replace IAM, endpoint policy, DNS, logging, or service-specific authorization. The current Bedrock documentation lists separate endpoint service names for bedrock, bedrock-runtime, bedrock-agent, bedrock-agent-runtime, and newer…

Generative AI applications often integrate with APIs, databases, SaaS tools, vector stores, GitHub, ticketing systems, payment services, and other systems that still require secrets. AWS recommends storing credentials and other sensitive values in AWS Secrets Manager rather than source code, images, prompts, environment files, or copied CI/CD variables. Secrets Manager encrypts secrets at rest with AWS KMS and returns them over TLS when authorized applications retrieve them. The best secret, however, is often the one the application never creates. IAM roles, workload identity, Bedrock AgentCore Identity, and service-native authentication can…

RAG architecture on Amazon Bedrock connects a user question to authoritative external evidence before a foundation model generates the answer. Amazon Bedrock Knowledge Bases can manage ingestion, embeddings, vector storage integration, retrieval, metadata filters, reranking, citations, structured-data queries, and Retrieve-and-Generate workflows, while applications can also use lower-level retrieval APIs when they need more control. The architecture decision is not simply “use a Knowledge Base.” Teams still need to choose source ownership, parsing, chunking, embeddings, vector storage, retrieval mode, metadata, authorization, reranking, prompt construction, model, citations, evaluation, refresh, and security. Managed…

RAG data poisoning occurs when malicious, misleading, unauthorized, or low-quality content enters the retrieval corpus and influences model responses later. The attack can be obvious, such as a fake policy document, or subtle, such as hidden text that contains an indirect prompt injection designed to make the model ignore developer instructions or misuse a tool. AWS security guidance emphasizes that RAG security must cover the ingestion pipeline as well as model inference. Bedrock Guardrails can detect supported direct prompt-attack patterns at inference time, but poisoned external content can enter earlier…

Amazon Bedrock Prompt management turns a prompt from an application string into a versioned AWS resource with variables, model or inference configuration, prompt variants, testing, comparison, and deployable versions. This matters because prompts can change production behavior as materially as code, yet informal teams often edit them directly in notebooks or environment variables without review or rollback. Current Bedrock Prompt management uses a mutable draft while the team iterates. When the prompt is ready for production, a version creates a snapshot that applications can reference. The console can compare versions…

Multi-agent workflows divide a complex task among specialized agents instead of asking one large agent to understand every domain, tool, and permission boundary. On AWS, the current architectural center for new agent development is Amazon Bedrock AgentCore. AWS moved Amazon Bedrock Agents into maintenance mode as “Bedrock Agents Classic” on July 30, 2026, and recommends AgentCore for new agent workloads and future migration. That platform shift matters for multi-agent design. Bedrock Agents Classic still supports supervisor and collaborator agents for existing customers, but AWS’s current maintenance-mode guidance says advanced multi-agent…

Latency in a Bedrock application is the sum of several components: authentication and API edge time, model queueing, prompt processing, generation, retrieval, reranking, tool calls, agent orchestration, network hops, and client rendering. Optimizing only the model invocation can produce little user-visible improvement when the real delay is a large retrieval query or three sequential agent actions. Amazon Bedrock provides several latency-related options, including streaming APIs, prompt caching, cross-Region inference, inference-profile routing, and a latency-optimized inference feature that AWS currently documents as preview for supported models and Regions. The useful approach…

IAM for generative AI applications is the boundary between “the model can reason about this operation” and “the AWS account actually permits this operation.” Amazon Bedrock, Knowledge Bases, AgentCore, Lambda tools, vector stores, S3 sources, KMS keys, Secrets Manager, and application services can each require different identities and permissions. Collapsing them into one broad execution role makes agent behavior hard to contain and harder to audit. The durable principle is least privilege with separate identities for humans, deployment automation, application runtime, retrieval, and high-impact tools. AWS Identity and Access Management…