SEC536: Adversarial AI - Penetration Testing AI Systems

In-Person
In-Person & Virtual
Organizations are deploying autonomous AI agents into production at scale: agents that authenticate, execute multi-step workflows, and make decisions across cloud infrastructure. These agents operate at machine speed, which means security operations must too.
When agents behave unexpectedly, the pattern closely resembles an insider threat, but traditional detection and response cycles were designed for human-speed investigation. In this session, we'll share what we're seeing across enterprise environments as agentic workloads go into production: where the detection gaps are, why observability and security telemetry must converge at the application layer, and how organizations are building response capabilities that contain and remediate autonomously in seconds, not hours. We'll walk through practical patterns for calibrating automated response: when to contain, when to escalate, and when to let the system close the loop. The goal is to make security as fast and autonomous as the workloads it protects.
In-Person & Virtual
The rapid shift to autonomous AI agents in the cloud is accelerating security concerns. Traditional, human-centric systems are overwhelmed by the millions of new digital identities agents create, exacerbating issues like compromised identities (the source of over 70% of cloud breaches) and sophisticated agent hijacking attacks.
This talk argues for the urgent application of Zero Trust architecture to AI infrastructure. We present a hybrid framework that anchors on three core, non-negotiable principles for securing AI agents: Human Oversight for governance and critical actions, Innovation with Control through dynamically limited privileges, and Transparency via centralized logging and auditability.
In-Person & Virtual
In-Person & Virtual
Agentic AI represents a structural shift in enterprise software, not an incremental improvement. For decades, organizations built security frameworks around a foundational assumption: humans make decisions, machines execute them. Agentic systems invert that model. They reason, plan, and act autonomously across identities, data stores, tools, and other agents, operating at machine speed in environments designed for human-paced decisions.
The security architecture most enterprises have in place was not built for this. Perimeter controls, identity governance frameworks, data classification policies, and audit mechanisms were designed for stateless, deterministic workloads. Agentic systems are stateful, adaptive, and capable of lateral tool invocation across an organization's most sensitive systems in a single workflow. The gap between existing controls and the actual risk surface of a production agentic deployment is not a configuration problem. It is an architectural one.
Secure agentic deployment requires six foundational governance primitives, applicable regardless of platform or vendor: agent identification and inventory, data labeling and policy enforcement, agent security posture management, conditional access for agentic workflows, runtime threat detection and remediation, and compliance reporting. These primitives are not new products to be purchased. They are the deliberate extension of Zero Trust principles to a new class of non-human actor, and they must be architected into agentic systems from day one rather than retrofitted after the first incident.
The organizations that adopt agentic AI safely will not be the ones with the most policies. They will be the ones that treat identity as the foundational control plane, govern the data agentic systems can reach before they govern what those systems generate, and build continuous adversarial testing into their security posture from the start. That window is open now, and it is the defining cloud security decision of this generation.
In-Person & Virtual
As autonomous AI agents move into production across cloud environments, security teams face a fundamental mismatch: agents authenticate, execute, and make decisions at machine speed, while many security architectures were designed for human-paced, deterministic workloads.
Frank Kim leads a discussion with security leaders from AWS, Google Cloud, and Microsoft on the realities of securing AI-driven cloud environments. Marking the fifth year of Cloud Security Exchange, the panel will explore how cloud security has evolved over the past five years and what comes next as organizations embrace increasingly autonomous systems.
In-Person & Virtual
In-Person & Virtual
In-Person & Virtual
Agentic Capture The Flag with Google SecOps
Step into the future of cyber defense. This hands-on experience is designed for security practitioners ready to move beyond AI hype into real-world application.
Learn how AI agents can help you investigate, triage, and respond to threats at machine speed through practical exercises, real-world scenarios, and an interactive CTF challenge.
Target Audience: Security practitioners, but decision makers and executives are welcome to join as well.
In-Person
Security teams have spent years automating repetitive work, but recent advances in agentic AI are enabling something different: autonomous analysts capable of investigating alerts, gathering evidence, making decisions, and taking action with minimal human involvement.
This session shares the blood, sweat, and tears of building autonomous security operations capabilities in the real world. We'll explore what worked, what surprised us, where autonomous agents excel, and where human judgment remains essential. As organizations increasingly deploy agents across security and IT functions, visibility and governance become just as important as automation. Attendees will leave with practical lessons for evaluating autonomous systems, understanding their operational risks, and maintaining control as AI-powered teammates become part of the SOC.
In-Person
As organizations adopt generative AI on AWS, security teams need to extend their investigation capabilities into the AI layer. In this hands-on workshop, participants work through a realistic multi-phase incident — tracing a compromised credential from initial exposure through privilege escalation into Amazon Bedrock knowledge bases and AI agents.
Using Amazon GuardDuty, AWS Security Hub, Amazon CloudTrail, and Amazon Athena, you'll reconstruct the full attack chain and walk away with the skills to detect, investigate, and respond when threat actors pivot from traditional cloud resources into generative AI workloads.
In-Person
Wrap up the day at the SANS Social, our evening networking event where great conversations continue after the day's presentations and workshops are over. Enjoy drinks, delicious food, and a relaxed atmosphere while connecting with fellow attendees, sharing ideas, and building new professional relationships. Whether you are catching up with colleagues or meeting someone new, the SANS Social is the perfect way to end the day.
In-Person
In-Person
In-Person & Virtual
We'll cover the latest on AI-enabled nation state activity from Anthropic's view, including Mythos-class models; frontier model cyber capabilities to help defenders succeed against AI-augmented attacks; and what the future of model scaling has in store for cyber, engineering, and SDLC right around the corner.
In-Person & Virtual
Machine identities now vastly outnumber humans in most Azure tenants, and they are routinely over-permissioned, long-lived, and sparsely monitored. When a service principal deletes diagnostic settings or a managed identity writes to a storage account, the analyst staring at the Activity Log has to answer a deceptively hard question: which identity actually did this, and on whose behalf?
The same operation can come from a human in the Portal, a human running az CLI commands, a custom OAuth app acting on a user's behalf, a service principal with a leaked secret, or a system- or user-assigned managed identity — and in the raw JSON these scenarios look far more alike than they should. Misreading that context is exactly how OAuth consent abuse, leaked SP credentials, and over-privileged managed identities slip past detections written for a human-centric world.
This session presents hands-on research mapping six concrete Azure identity scenarios — user via CLI, user via Portal, service principal with application permissions, service principal with delegated permissions, system-assigned managed identity, and user-assigned managed identity — to the exact claims each emits. The result is a three-layer identity-context model that separates human from non-human actors, distinguishes credentialed service principals from Azure-managed identities (flagging impossible combinations as anomalies), and pinpoints the parent resource behind a managed identity so legitimate automation can be whitelisted with precision.
Attendees leave with an annotated identity claims matrix, a SIEM-agnostic decision tree, and a tiered starter ruleset they can deploy against their own tenants the week they get home.
In-Person & Virtual
Most teams integrating LLMs into cloud applications are flying blind in their pipelines. They have added scanners and perhaps some guardrails, but they do not actually know whether those controls hold up against real attacks. This session explores a practical approach to closing that gap.
The idea is simple: treat AI/LLM security like any other part of the pipeline, something that can be measured, tested, and broken before it reaches production.
Attendees will see a working setup that ingests CI/CD configurations (GitHub Actions, GitLab CI) and builds a basic risk picture: what controls are missing, where data could leak, and how exposed the LLM layer actually is. It is not perfect. Mapping pipelines to meaningful risk is messy, and there is a fair amount of noise, but it is enough to surface the problems most teams are not seeing.
From there, attack simulation is layered into the process. Instead of assuming prompt filtering or guardrails work, the controls are actively tested during the pipeline. Prompt injection, data exfiltration, and system prompt leakage are evaluated as part of the build. In many cases, the first run fails, which is the point.
Once controls are added or tightened, the same tests are re-run and used as the signal for whether a build should pass. This shifts security from a checklist-based exercise to a validation-driven process.
This is not a silver bullet. LLM behavior is inconsistent, false positives occur, and scaling this approach across multiple pipelines takes effort. However, it provides a practical way to move from "we think it is secure" to "we have actually tested it." Attendees will leave with a concrete pattern they can adapt: how to map pipeline risk, how to simulate real LLM attacks, and how to integrate both into CI/CD workflows without slowing teams down.
In-Person & Virtual
In-Person & Virtual
In-Person & Virtual
While the security community remains hyper-focused on LLM-specific threats like prompt injection, real-world enterprise deployments reveal that the most critical vulnerabilities often lie in the underlying architecture. This session explores hard-learned lessons from several Generative AI (GenAI) deployments, moving past theoretical risks to address the infrastructure failures that break production environments.
Three primary architectural pillars will be deconstructed:
The Compute Layer: How under-provisioned containerized backends create self-inflicted Denial of Service (DoS) conditions and how to implement auto-scaling guardrails.
The Networking Layer: Navigating the "VPC Lockout" phenomenon, where aggressive data exfiltration controls break Retrieval-Augmented Generation (RAG) workflows.
The IAM Layer: Solving the "God-Mode" dilemma in Agentic AI by implementing automated least-privilege for service accounts with broad agency.
Attendees will leave with a technical blueprint for securing RAG and Agentic workflows, including specific strategies for Layer 7 filtering, VPC Service Control tuning, and Infrastructure-as-Code (IaC) patterns to remediate over-privileged AI service accounts.
Key Takeaways for Attendees:
Root-Cause Methodology: Techniques for using cloud-native monitoring to distinguish between model hallucinations and infrastructure-induced latency.
Network Policy Tuning: How to design allow-lists for AI agents and external vector databases without compromising data exfiltration boundaries.
Agentic IAM Governance: Practical application of IAM recommenders and IaC to strip "God-Mode" permissions from AI service accounts.
Defense-in-Depth: Implementing Layer 7 filtering to block malicious inputs before they reach the model endpoint.
In-Person & Virtual
When an AI is 70% accurate at automating each task of a 10-task investigation, 97% of cases end up incomplete, and that is the optimistic scenario. This session explores efforts to close that gap for cloud and SOC investigations.
Using CTFs as one of the challenge sets, the work achieved the first public agentic speed-run of Splunk Boss of the SOC. Along the way, an unexpected challenge emerged: frontier AI models were breaking the evaluation framework itself. This session examines what it takes to make agentic investigations perform measurably well and how to gain confidence that the results are meaningful.
The first half of the session systematically walks through how to push AI investigators from barely passing to achieving 96%+ performance. Starting with accessible off-the-shelf tools such as Claude Code as a baseline, the discussion layers on the methods that effectively tackle cloud investigation tasks in BOTS, including leaky S3 buckets, identity pivots, and the types of multi-source correlation that real cloud SOCs perform daily. Topics include the impact of model selection, semantic layers over log data, agent harness design, MCP server choices, and prompting patterns that materially improve results. The session also highlights which established best practices have become obsolete, what is now viable with open-source models and modern agent harnesses, and the cost and latency tradeoffs associated with each approach. More fundamentally, the discussion zooms out to the organizational implications and how teams should structure rollout and iteration.
Measurement is foundational, so the second half focuses on evaluation frameworks for investigations. An emerging theme driven by advances in frontier models is that modern evaluations must now be treated adversarially. Frontier agents are increasingly capable of returning correct answers without ever touching the logs, effectively learning how to bypass the intended evaluation process. By understanding how agents circumvent evaluations, teams can build stronger measurement frameworks and, through them, more reliably improve their automations.
Attendees will leave with a practitioner's playbook for deploying agentic AI investigators in cloud environments that perform reliably, a framework for comparing techniques at a more fundamental level, and botsbench.com as an open resource for benchmarking and detecting contamination in their own pipelines.
In-Person & Virtual
In-Person & Virtual
Your AI systems are traveling at Warp 9. Your security defenses are stuck at impulse power. By 2026, enterprises face 109 autonomous agents for every human, threats evolving monthly, 3-6 month technology refresh cycles. Traditional security planning (18-24 month cycles) can't keep up.
Based on real-world experience securing AI systems at scale at Salesforce, this talk addresses both the technical patterns that work AND the organizational transformation required to operate at AI speed.
Technical: Why three-layer defense (IAM + Perimeter + Guardrails) is mandatory. How API-layer enforcement blocks exfiltration from authenticated agents. Real attack patterns: prompt injection (68.8% violation rate) and supply chain attacks. Which frameworks matter: NIST AI RMF, CSA AI Controls Matrix, OWASP LLM Top 10.
Leadership: How to enable safe experimentation through risk-tiered environments. Why engineers should only do new things (repetitive work becomes shared skills). Moving from activity metrics to outcome metrics. Scaling from managing 50 people to supervising 200+ agents. Implementing weekly deployment cadence.
Attendees leave with actionable frameworks: three-layer architecture, risk-based experimentation, 60-day fail-fast cycles, outcome metrics, and weekly deployment roadmaps.
In-Person & Virtual
In-Person & Virtual
In-Person & Virtual
Agentic Capture The Flag with Google SecOps
Step into the future of cyber defense. This hands-on experience is designed for security practitioners ready to move beyond AI hype into real-world application.
Learn how AI agents can help you investigate, triage, and respond to threats at machine speed through practical exercises, real-world scenarios, and an interactive CTF challenge.
Target Audience: Security practitioners, but decision makers and executives are welcome to join as well.
In-Person
Security teams have spent years automating repetitive work, but recent advances in agentic AI are enabling something different: autonomous analysts capable of investigating alerts, gathering evidence, making decisions, and taking action with minimal human involvement.
This session shares the blood, sweat, and tears of building autonomous security operations capabilities in the real world. We'll explore what worked, what surprised us, where autonomous agents excel, and where human judgment remains essential. As organizations increasingly deploy agents across security and IT functions, visibility and governance become just as important as automation. Attendees will leave with practical lessons for evaluating autonomous systems, understanding their operational risks, and maintaining control as AI-powered teammates become part of the SOC.
In-Person
As organizations adopt generative AI on AWS, security teams need to extend their investigation capabilities into the AI layer. In this hands-on workshop, participants work through a realistic multi-phase incident — tracing a compromised credential from initial exposure through privilege escalation into Amazon Bedrock knowledge bases and AI agents.
Using Amazon GuardDuty, AWS Security Hub, Amazon CloudTrail, and Amazon Athena, you'll reconstruct the full attack chain and walk away with the skills to detect, investigate, and respond when threat actors pivot from traditional cloud resources into generative AI workloads.
In-Person
Generative AI is quickly becoming a default part of the software development lifecycle. Faster delivery can also increase security risk, operational fragility, and decisions that are difficult to review or audit after the fact.
This session focuses on AI coding guardrails: practical, developer-friendly controls that help teams adopt coding assistants with confidence while maintaining strong security, governance, and assurance. We will walk through four maturity levels for agentic coding controls.
Level 1 uses built-in steering mechanisms in modern agentic IDEs, including project context files, rules, and instruction layers. These establish consistent expectations for architecture, security patterns, data handling, logging, and “how we do things here,” so teams reduce variability and improve repeatability.
Level 2 adds context-injection agents that deliver guidance at the right time. They provide the relevant requirements, approved patterns, and constraints based on what is changing, the type of work underway, and where the developer is in the workflow.
Level 3 adds deterministic, policy-as-code guardrails in pull requests and CI/CD to enforce security, quality, and compliance requirements with measurable outcomes and audit-ready evidence.
Finally, we hit level 4, where everything implemented in levels 1 through 4 continuously improves to strengthen controls over time.
In-Person & Virtual