Show Notes
In this episode of BHIS Presents: AI Security Ops, the team digs into a problem every AI-enabled SOC eventually hits: The demo looked great — until the inference bill showed up! AI in SecOps gets expensive because security data is huge, repetitive, and constant. Logs, alerts, runbooks, tool definitions, and historical context all get pushed into models again and again. That burns money, slows systems down, and often makes answers worse. The fix is not exotic. It is basic engineering: use smaller models where they work, cache what repeats, stop dumping raw logs, and save expensive reasoning for the cases that actually need it. We dig into: • Why AI SecOps workloads get expensive fast • When smaller models are good enough • Where frontier models still make sense • How grouping alerts into cases reduces waste • Using strong models to judge cheaper models • Why prompt caching can be a major cost lever • How small prompt changes can break caching • Batch APIs for non-urgent security work • Why raw logs make prompts noisy and expensive • RAG, deduplication, and cached verdicts • Budget caps, circuit breakers, and stolen-key risk • When deterministic code beats another model call AI cost control is not just a budgeting exercise. It is a security architecture issue. If every alert goes to the biggest model with no caching, no limits, and no measurement, the system is not just expensive — it is uncontrolled. Good AI SecOps design means scoping the model, reducing unnecessary context, measuring spend, and putting guardrails around how AI is allowed to operate. ⸻ 📚 Key Concepts & Topics AI Cost Architecture • SecOps cost comes from large inputs, repeated context, and high alert volume • Model selection should match task difficulty • Routine triage can often use smaller models • Hard correlation and judgment may justify stronger models Model Evaluation • Test smaller models against real historical cases • Use stronger models as judges when appropriate • Compare quality before moving workloads • Do not assume the biggest model is always necessary Prompt & Context Design • Cache static instructions, tool definitions, and repeated context • Keep cacheable sections stable • Avoid changing static prompts with unnecessary variables • Better prompt structure can reduce both cost and noise Data Reduction & Retrieval • Do not send entire logs when only a few fields matter • Preprocess alerts before model calls • Use RAG instead of stuffing whole libraries into prompts • Cache repeated verdicts for repeated alert patterns Operational Guardrails • Track AI spend by workload • Set hard caps and circuit breakers • Use limits to reduce stolen-key blast radius • Treat AI pipelines like production security systems Deterministic Workflows • Not every task needs inference • Repeatable logic should become code • AI can help write that code • Once the workflow is deterministic, stop paying the model to repeat it #AISecurity #LLMSecurity #CyberSecurity #ArtificialIntelligence #SecOps #SOC #InfoSec #BHIS #AppSec #PromptEngineering #securityarchitecture ---------------------------------------------------------------------------------------------- About Brian Fehrman - https://www.blackhillsinfosec.com/team/brian-fehrman/ About Bronwen Aker - https://www.blackhillsinfosec.com/team/bronwen-aker/ About Derek Banks - https://www.blackhillsinfosec.com/team/derek-banks/ About Ethan Robish - https://www.blackhillsinfosec.com/team/ethan-robish/ About Ben Bowman - https://www.blackhillsinfosec.com/team/ben-bowman/ (00:00) - Intro: When the AI Triage Assistant Gets Expensive
(01:27) - The Setup: Saving Money Without Killing the Workflow
(02:22) - Right-Size the Model: Cheap for Routine, Big for Hard
(05:36) - Testing Smaller Models, Judges & Real SOC Workflows
(13:46) - Prompt Caching: The Big Lever Hiding in Plain Sight
(18:37) - Batch APIs: Half the Urgency, Lower the Cost
(20:19) - Stop Dumping Logs: Less Noise, Better
More from AI Security Ops
In this episode of BHIS Presents: AI Security Ops, the team digs into some of the strangest recent examples of AI behaving badly. An AI agent finds a way around its sandbox using DNS. Malware asks four different AI models what it should do next. Attackers overwrite an AI agent’s “soul.” And a placeholder domain buried across GitHub becomes an unexpected security risk. Funny stories? Absolutely. But underneath them are some very familiar security problems. We dig into: - An OpenAI agent finding an unexpected path out through DNS - Whether a 2.5-hour response is fast enough in the age of AI - CLOSEDQUORUM malware using multiple AI models to make decisions - Why outbound AI API traffic could become a useful detection signal - The Carbonado botnet rewriting an agent’s SOUL.md instructions - The risks of exposed Docker daemons - How an unreserved placeholder domain can become an attack vector - Why AI agents make old security mistakes more consequential The takeaway: none of these techniques are particularly new. DNS tunneling, exposed services, compromised configuration files, and untrusted domains have existed for years. AI just gives attackers — and occasionally AI agents themselves — new ways to find and exploit those boring gaps at greater speed and scale. 2026-09-29-News (1).docx (00:00) - Intro: AI Behaving Badly
In this episode of BHIS Presents: AI Security Ops, the team continues its breakdown of the OWASP Agentic Skills Top 10, covering risks six through ten. Agentic skills may look like simple instructions, but they operate inside AI systems with access to tools, files, networks, and other resources. So what happens when those skills aren’t isolated, verified, monitored, or governed? We dig into: - AST06: Weak Isolation - AST07: Update Drift - AST08: Poor Scanning - AST09: No Governance - AST10: Cross-Platform Reuse - Why sandboxing the agent isn’t the same as isolating individual skills - The security tradeoffs between automatic updates and outdated skills - Why traditional scanners struggle with malicious instructions written in plain English - The importance of maintaining an inventory of agentic skills - How security controls can disappear when skills move between platforms The takeaway: securing agentic skills requires more than checking them once before installation. Organizations need visibility into what skills are running, what they can access, how they change, and whether their security controls survive across environments. OWASPAgenticSkils-Part2.docx This is Part 2 of our look at the OWASP Agentic Skills Top 10, covering AST06 through AST10. Part 1 can be found here #AISecurity #CyberSecurity #AgenticAI #AIAgents #OWASP #LLMSecurity #InfoSec #BHIS #Antisyphon
In this episode of BHIS Presents: AI Security Ops, the team tackles a big question: Could AI actually take over the internet? Recent warnings from AI industry leaders have raised concerns about autonomous agent swarms escaping containment, compromising systems, and operating at a scale humans may struggle to match. But what would “taking over the internet” actually require? The team looks at the problem from a hacker’s perspective — examining compute requirements, persistence, botnets, vulnerable infrastructure, the recent Hugging Face incident, and the difference between AI acting autonomously and humans using AI to accelerate attacks. We dig into: - Whether an autonomous AI swarm could realistically operate at internet scale - What the Hugging Face incident actually tells us about agentic security - The compute and infrastructure required to sustain a rogue agent swarm - How botnets and compromised systems could change the equation - Why existing vulnerabilities may be a bigger concern than hypothetical AI takeover scenarios - Whether slowing frontier AI development actually addresses the cybersecurity problem - The difference between AI exhibiting dangerous behavior and AI having intent - Why defenders need to focus on their real-world attack surface today The takeaway: AI is dramatically increasing the speed and scale of cybersecurity operations, but powerful models still operate inside systems designed, deployed, and connected by people. The immediate security question may be less “Will AI take over?” and more “What are we giving AI access to?”
In this episode of BHIS Presents: AI Security Ops, the team breaks down the first five risks in the OWASP Agentic Skills Top 10 (Part 1). Agentic skills can give AI systems reusable instructions, workflows, and capabilities — but they also introduce a new attack surface. A skill may look like a simple markdown file, yet the agent following those instructions could have access to your filesystem, credentials, network, shell, or other sensitive resources. We dig into: - AST01: Malicious Skills - AST02: Supply Chain Compromise - AST03: Overprivileged Skills - AST04: Insecure Metadata - AST05: Untrusted External Instructions - Why skills should be treated more like software than configuration - How excessive permissions increase an agent’s blast radius - Why external content creates indirect prompt injection risks - How isolation, least privilege, and trusted sources can reduce risk The takeaway: “just markdown” isn’t necessarily harmless when an AI agent can act on what it reads. This is Part 1 of our look at the OWASP Agentic Skills Top 10, covering AST01 through AST05. — Learn more about Black Hills Information Security: https://www.blackhillsinfosec.com/ Check out Antisyphon Training: https://www.antisyphontraining.com/ (00:00) - Intro: OWASP Agentic Skills Top 10, Part 1
Agentic skills can make AI agents more capable and consistent—but they can also introduce serious security risks. This episode explains how skills work, why malicious skills rank as a leading OWASP concern, and how seemingly harmless Markdown instructions can enable credential theft, remote payload delivery, and manipulated recommendations. Real-world examples illustrate how malicious skills can evade scanners and exploit trusted marketplaces. The episode concludes with practical safeguards, including reviewing skill files, watching for external instructions and prompt injection, pinning versions, limiting permissions, and running agents inside isolated environments. Links: OWASP Agentic Skills Top 10 Malicious AI Agent Skill Bypasses Security Scans and Seizes Full Control of Over 26,000 Agents (00:00) - Agentic Skills and the OWASP Top 10