Stackcurve Research Library
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Bias and Fairness in Enterprise AI: What the Regulations Actually Require
AI bias has been a research topic for years. It is now a regulatory requirement. Here is what the major frameworks actually mandate — and what 'fairness' means in a compliance context.
How Attackers Use Your VPN Against You
VPN has become one of the most reliably exploited entry points in enterprise breaches. The architecture that was designed to protect remote access has become a privilege escalation vector. Here is why.
RAG Security: The Attack Surface Inside Your Enterprise AI
You built a RAG system to make your AI smarter. You may have also built an attack surface your security team has never reviewed.
Agent Sprawl: The Enterprise Governance Problem Nobody Is Tracking
AI agents are being deployed faster than governance programs can track them. Agent sprawl — ungoverned agents with undocumented permissions and undefined accountability — is the 2026 version of shadow IT. Here is how it develops and how to govern it.
Training Data Poisoning: How Attackers Corrupt Models Before Deployment
Training data poisoning is the AI equivalent of a supply chain attack. An attacker who can influence the data a model trains on can influence the model's behavior in production — often without detection. Here is what enterprises need to know.
External Attack Surface: The Assets You Don't Know You Have
The assets that get breached are often not the ones on the security team's radar. Here is what external attack surface management finds that traditional asset inventories miss — and why the gap keeps growing.
AI Infrastructure Costs: Why the First Bill Is Always a Surprise
Enterprise AI infrastructure costs are consistently underestimated at procurement — then consistently shocking at first invoice. Here is the complete cost structure most buyers miss.
Hallucination as a Business Risk: When Wrong AI Output Creates Liability
AI hallucination is treated as a technical limitation. It is also a legal liability, a reputational risk, and an operational failure mode. Here is how enterprises should govern it.
Branch Office Blind Spots: The Security Gap in Distributed Enterprises
Distributed enterprises built their network security around hub-and-spoke architectures that no longer match how users work. Here is where the visibility gaps are and how SASE closes them.
Model Theft Is Real — And Most Enterprises Have No Defense
Your fine-tuned AI model represents months of investment and proprietary data. Are you protecting it like the asset it is?
Memory, Context, and State in Enterprise AI Agents: The Infrastructure Underneath
AI agents that can remember previous interactions, maintain state across sessions, and build contextual understanding over time are categorically more capable than stateless agents. They also require infrastructure that most enterprise teams haven't planned for.
Data Residency and AI: When Your Model Training Violates Your Data Agreements
Enterprise AI training pipelines regularly cross data residency boundaries that existing data governance agreements were not written to address. Here is where the conflicts arise and how to govern them.
The Five Stages of CTEM: Scoping, Discovery, Prioritization, Validation, Mobilization
Gartner's CTEM framework defines five operational stages. Most enterprises implement one or two. Here is what each stage requires and why the sequence matters.
LLM Deployment Models: SaaS API vs. Managed Service vs. Self-Hosted
The decision of how to deploy your LLM determines your cost structure, your data privacy posture, your customization ceiling, and your operational burden. Here is how to make it correctly.
Building an AI Inventory: The First Governance Control Nobody Has
You cannot govern what you haven't inventoried. Most enterprises deploying AI have no systematic inventory of their AI systems, the data they use, or the decisions they influence. Here is how to build one.
What Single-Vendor SASE Actually Means (And What Vendors Are Faking)
Every major network security vendor now claims to offer single-vendor SASE. Most are offering acquisitions stitched together with a shared login. Here is how to tell the difference.
What "AI-Native Security" Actually Means (And What Vendors Are Faking)
Every security vendor now claims to be AI-native. Most are not. Here is how to tell the difference before you sign a contract.
Human-in-the-Loop vs. Fully Autonomous: The Oversight Decision That Changes Everything
The degree of human oversight in an AI agent deployment determines its risk profile, its governance requirements, and its liability exposure. Most enterprise teams are making this decision implicitly. Here is the framework for making it explicitly.
Inference-Time Data Exposure: What Happens to Your Data When the Model Runs
Every time your enterprise AI system processes a user query, data flows through an inference pipeline that may touch external APIs, retrieve from internal databases, and generate output that crosses organizational boundaries. Here is the data exposure map.
Prioritization Over Patching: Why CTEM Changes the Vulnerability Workflow
The enterprise vulnerability backlog is infinite. The patching capacity is not. CTEM's core contribution is a prioritization framework that concentrates remediation effort where it reduces attacker success probability the most.
AI Inference vs. AI Training: Why the Compute Requirements Are Completely Different
Training and inference are both 'running AI' in the same way that building a factory and operating a factory are both 'manufacturing.' The infrastructure, the cost model, and the optimization priorities are entirely different.
The Board's AI Governance Responsibility: What Directors Are Liable For
AI governance has reached the boardroom — not as a technology discussion but as a fiduciary responsibility. Here is what the legal and regulatory landscape says directors are accountable for.
Shadow AI Is the New Shadow IT — And Your CASB Wasn't Built for It
CASB solved the shadow IT problem of 2019. The shadow AI problem of 2026 is different in kind, not just degree. Here is what your existing CASB misses and what to do about it.
The Five AI Security Controls Every Enterprise Should Have in Place Today
The AI security market has over 140 vendors and a dozen sub-categories. If you don't know where to start, start here.
Tool Access and Permissions: The Governance Layer Every Agent Platform Needs
An AI agent's capability is determined by the tools it can use. Its risk profile is determined by the permissions those tools carry. Most enterprise agent deployments grant too much access and audit too little. Here is the governance framework.
Training Data Security: The Risk Profile Nobody Has Mapped
The training data for your AI models is one of the highest-value and least-protected data assets in the enterprise. Here is the complete risk profile and what security controls actually apply.
Attack Surface Management: The Continuous Inventory Problem
You cannot manage exposure on assets you don't know you have. Attack surface management — continuous discovery of the enterprise's externally exposed assets — is the foundational CTEM capability most enterprises haven't built.
On-Premises vs. Cloud AI Compute: The Tradeoffs Nobody Tells You Upfront
The on-prem vs. cloud decision for AI compute has a different calculus than it does for traditional enterprise workloads. Here is the analysis most enterprises skip.
You Are Your Own Worst Leak
Counter-intelligence veteran Kenneth Vignali explains why the most damaging security breach in your organization is the one your marketing team published on purpose — and what to do before AI-assisted competitors exploit it.
AI Risk Management vs. AI Governance: Why Enterprises Confuse the Two
AI risk management and AI governance are related but distinct disciplines. Buying a governance platform when you need a risk framework — or vice versa — produces a program with systematic blind spots.
The VPN Replacement Decision: When ZTNA Makes Sense and When It Doesn't
ZTNA has clear advantages over legacy VPN — but the migration is not simple, and there are use cases where VPN still wins. Here is how to make the right call.
Shadow AI: The Inventory Problem Your Security Team Hasn't Solved
Before you can secure your AI, you have to know what AI you have. Most enterprises don't. Here is how to find it — and what to do when you do.
The Agent Orchestration Problem: Why Single-Agent Deployments Aren't Enterprise-Ready
A single AI agent can answer questions and complete tasks. Enterprise workflows require multiple agents working in coordination — and the orchestration layer that makes that work is the hardest part of enterprise agent deployment.
The Data Lifecycle in AI Systems: Where Protection Must Be Applied
Data in an AI system moves through a lifecycle that is categorically different from data in a traditional enterprise application. Identifying where sensitive data flows in that lifecycle is the prerequisite for protecting it.
The Exposure Management Maturity Model: Where Most Enterprises Actually Stand
The gap between where enterprises believe they are in exposure management and where they actually are is the most consistent finding in Stackcurve's CTEM research. Here is the honest maturity framework.
GPU vs. CPU vs. TPU: The Hardware Decision That Drives Everything Else
Every enterprise AI deployment starts with a hardware question most teams answer wrong. Here is what the accelerator options actually mean for your workload.
The EU AI Act Is Now Enforceable: What Your Legal Team Needs to Know
The EU AI Act's prohibited practices provisions took effect in February 2025. High-risk AI system requirements follow in August 2026. Here is what your legal team needs to have ready.
Zero Trust Is Not a Product: Why Enterprises Keep Buying the Wrong Thing
Zero trust has become the most overloaded term in enterprise security. Here is what it actually means, what it requires, and how to stop buying vendor claims instead of architecture.
Why Your Existing AppSec Stack Won't Protect Your AI Applications
Your WAF, SAST scanner, and DAST tools were built for a different threat model. Here is what they miss — and what you need instead.
What an Enterprise AI Agent Platform Is — and What You're Actually Buying
Enterprise AI agent platforms have become one of the fastest-growing categories in enterprise software. The definitions are loose, the capabilities vary widely, and the marketing is ahead of the reality. Here is the honest category definition.
What 'Data Security for AI' Means — and Why It's Different from Traditional DLP
Traditional data loss prevention was designed for structured data moving through defined channels. AI systems process, generate, and transform data in ways that DLP was not designed to govern. Here is the new data security problem.
What CTEM Is — and Why Vulnerability Management Isn't It
Continuous Threat Exposure Management is not an upgrade to vulnerability management. It is a different discipline built on a different premise. Here is the distinction that matters for enterprise security investment.
The AI Infrastructure Stack: What You're Actually Buying When You Buy 'AI'
Enterprise AI deployments touch compute, storage, networking, data pipelines, model serving, and observability. Most buyers optimize for one layer and underfund the rest. Here is the full stack.
What AI Governance Actually Means in 2026 — and What It Doesn't
AI governance has become a boardroom priority without a boardroom definition. Here is what the term actually covers, what it doesn't, and why the confusion is costing enterprises time and money.
SASE vs. SSE: What the Acronym War Is Hiding from Enterprise Buyers
Every SASE vendor claims to offer complete secure access. Most are selling you half of the architecture. Here is what the acronyms actually mean and how to use them to buy correctly.
Prompt Injection Is Not a Research Problem — It's Your Problem Now
Prompt injection has moved from academic curiosity to active enterprise risk. Here is what it is, why your current controls miss it, and what to do about it.
The Agent Governance Gap No One Is Talking About
Enterprises are deploying AI agents at speed. Almost none of them have built the authorization, audit, and scope enforcement layer those agents require. The liability is compounding quietly.
SASE Consolidation Is Moving Faster Than Analysts Predicted
Three years ago, the consensus view was that SASE consolidation would take a decade. The market has compressed that timeline dramatically — and the implications for enterprise buying teams are significant.
The EU AI Act Compliance Clock: What Enterprise IT Buyers Need to Know Now
The EU AI Act's high-risk AI system requirements are now in effect for many enterprise deployments. Most organizations are significantly behind where they need to be.
Five Questions Every CISO Should Ask Before Deploying Production AI Agents
The pressure to deploy AI agents in production is real. So is the security gap between what agent platforms promise and what enterprise security teams should actually accept.
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