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In this episode, Jason Haddix (CEO of Arcanum Information Security and creator of the Bug Hunter’s Methodology) joins us to examine how AI is changing penetration testing and security research. He explains that while AI agents can automate reconnaissance, code analysis, and parts of vulnerability discovery, meaningful results still depend on human expertise, methodology, and context engineering. The conversation explores how AI is shifting the entry path for new security practitioners, why deep research and critical thinking remain essential skills, and how experienced testers are embedding their knowledge into agent workflows using tools like Claude Code. Jason also discusses practical experimentation with AI assistants such as OpenClaw, including prompt-injection defenses, guardrails, and the operational risks of running autonomous systems. The episode also addresses the growing debate around AI-generated code and AI-driven vulnerability discovery, highlighting the difference between marketing claims and real-world results. It closes with a discussion on why the industry needs better benchmarks and evaluation methods to measure whether AI security tools actually find meaningful vulnerabilities.

In this episode, we examine what is shifting in AI, AppSec, and product security and what remains fundamentally the same. For years, application security has operated on a familiar model: siloed reviews, tool-driven findings, and periodic assessments that struggle to keep pace with modern development. AI doesn’t eliminate those pressures, it amplifies them. Code is generated faster, systems are more interconnected, and the surface area of change expands weekly.  The conversation explores agent-based workflows through tools like OpenClaw, not as novelty, but as a signal of a broader shift: from manually operating tools to orchestrating fleets of agents. As AI interfaces move from chat windows to terminals to messaging environments, security teams must reconsider where workflows live and how context is preserved across them. For decades, AppSec has struggled to build a reliable understanding of what systems exist and how they connect. Large language models may finally make it possible to construct living maps of components, data flows, and trust boundaries  enabling assessments that talk to each other instead of existing in isolation. The discussion also revisits threat modeling, not as a compliance artifact, but as a foundation for system-wide reasoning. If AI can automate baseline coverage and reduce repetitive toil, security teams may return to their original purpose: high-leverage risk judgment on critical systems. This leads to a broader debate whether AppSec as a distinct function evolves, shrinks, or dissolves into engineering itself and what the enduring “maker–checker” model of risk management demands in an AI-native world. Finally, the episode reflects on the role of large AI labs in security: the gap between ambitious claims and shipped products, and what that means for founders and security leaders navigating change. 00:00–02:15 — Why this is a no-guest episode & what’s changed since last year 02:15–06:30 — AI co-authoring, productivity gains, and writing workflows 06:30–10:20 — OpenClaw architecture, agent risks, and prompt injection realities 10:20–14:00 — The shifting UI of AI: chat → terminal → messaging agents 14:00–18:30 — Agent orchestration vs siloed security tooling 18:30–23:00 — Context graphs and assessments that “talk” to each other 23:00–27:30 — Threat modeling’s evolution and system-wide visibility 27:30–31:00 — Why inventory is still AppSec’s hardest problem 31:00–34:30 — Personal AI stacks: Obsidian, memory layers, and query tools 34:30–37:30 — Open source in the age of AI-generated PR spam 37:30–40:00 — AI labs: what they ship vs what they say 40:00–44:00 — Will AppSec disappear? A serious debate 44:00–48:00 — Maker–checker risk models in an AI-driven org 48:00–51:00 — Where AI replaces toil — and where humans stay critical 51:00–End — 2026 predictions for AI security and product security .

In this episode, we sit down with Jens to explore why AI agents fundamentally break traditional security assumptions, from API keys and browser sessions to composability and access control. Drawing parallels to DeFi exploits and smart contract failures, he explains why agent identity, short-lived delegated authorization, and zero trust aren’t optional add-ons, but the foundation for safely running autonomous systems. We also dive into context compression as both a performance and security challenge, the real difference between MCP and skills, and a future where humans may stop reviewing code altogether. As agents become the primary actors on the internet, even writing itself begins to change in an AI-scraped world. If agents are non-deterministic by design, the real question becomes: where do we reintroduce determinism? Tune in for a deep dive! Connect with Jens Ernstberger: Website: https://ernstberger.xyz/ LinkedIn: https://www.linkedin.com/in/jens-ernstberger-phd-96b0ba14a/Connect with Anshuman: LinkedIn: ⁠⁠⁠⁠⁠⁠anshumanbhartiya⁠⁠ X: ⁠⁠⁠⁠⁠⁠https://x.com/anshuman_bh⁠⁠ Website: ⁠⁠⁠⁠⁠⁠https://anshumanbhartiya.com/⁠⁠ ⁠⁠⁠⁠Instagram: ⁠⁠anshuman.bhartiya⁠ ⁠⁠⁠Connect with Sandesh: LinkedIn: ⁠⁠⁠⁠⁠⁠anandsandesh⁠⁠ X: ⁠⁠⁠⁠⁠⁠https://x.com/JubbaOnJeans

In this episode, Ankur Chakraborty discusses the evolution of AI security, emphasizing the importance of foundational security principles in the context of generative AI. He explores the challenges of scaling security measures in an era of rapid feature deployment and the necessity of integrating AI tools into security practices. The conversation delves into the balance between human oversight and autonomous systems, the significance of context in security decision-making, and the evaluation of security tools based on their outcomes. The discussion highlights the need for better guardrails and the role of context engineering in enhancing security practices.

In this conversation, Ian Livingstone discusses the changing landscape of AI and security, focusing on the challenges of agent identity and the need for a new approach to application security. He emphasizes the importance of understanding the non-deterministic nature of AI agents and the implications for identity management, permissions, and data security. The discussion also touches on the future of agent identity, the role of insurance in managing risks, and the transformation of security practices in the age of AI. Tune in for a deep dive! Connect with Ian Livingstone: LinkedIn: ⁠https://www.linkedin.com/in/irlivingstone/ Twitter: https://x.com/ianlivingstone Connect with Anshuman: LinkedIn: ⁠⁠⁠⁠⁠anshumanbhartiya⁠ X: ⁠⁠⁠⁠⁠https://x.com/anshuman_bh⁠ Website: ⁠⁠⁠⁠⁠https://anshumanbhartiya.com/⁠ ⁠⁠⁠⁠Instagram: ⁠anshuman.bhartiya ⁠Connect with Sandesh: LinkedIn: ⁠⁠⁠⁠⁠anandsandesh⁠ X: ⁠⁠⁠⁠⁠https://x.com/JubbaOnJeans⁠

In this episode, we sit down with Kane Narraway to unpack how enterprise security is changing as AI, platforms, and developer-driven security become the norm. Kane shares his path from digital forensics to leading security at Canva, and why understanding company culture matters just as much as choosing the right tools. We discuss why modern security is becoming platform-first, why much of the security vendor market optimizes for finding problems rather than fixing them, and why Kane believes security teams need more engineers and fewer manual processes.The conversation also digs into AI security, shadow IT (and shadow AI), and the real-world trade-offs between usability and control, especially as low-code and no-code tools become more common inside companies. Tune in for a deep dive! Connect with Kane Narraway: LinkedIn: https://www.linkedin.com/in/kane-n/ Blog: https://kanenarraway.com/ Connect with Anshuman: LinkedIn: ⁠⁠⁠⁠anshumanbhartiya X: ⁠⁠⁠⁠https://x.com/anshuman_bh Website: ⁠⁠⁠⁠https://anshumanbhartiya.com/ ⁠⁠⁠⁠Instagram: anshuman.bhartiyaConnect with Sandesh: LinkedIn: ⁠⁠⁠⁠anandsandesh X: ⁠⁠⁠⁠https://x.com/JubbaOnJeans

In this episode, we sit down with Travis McPeak, one of the most prominent thinkers in the space of developer security. Travis, who built his career at the intersection of security automation and developer productivity, shares his philosophy on achieving security at scale in the AI era. His career spans security leadership roles at major tech companies, including Symantec, IBM, Netflix, and Databricks. Most recently, he founded and served as CEO of Resourcely, a startup built on the idea of making cloud infrastructure secure by default, before being "acqui-hired" by Cursor, the rapidly growing AI-powered code editor, to lead security and enterprise readiness. Key Takeaways AI for Secure by Default: AI tools provide the best injection point to shift security "all the way left" and move past the reactive "whack-a-mole" approach, because developers are already motivated to use these highly effective tools.Changing AppSec Strategy: AI dramatically changes the nature of AppSec by making previously unscalable strategies, such as threat modeling, applicable. AI can generate architecture diagrams on demand by tracing through code.The Compliance Bottleneck: The dramatic consolidation of cloud security vendors reflects how compliance-minded the security industry remains. Critical infrastructure misconfigurations (like public databases being left open) often go unaddressed because they are not measured by compliance standards.Platform vs. Point Solutions: Travis argues against platforms that are often amalgamations of poorly integrated acquired tools. He suggests buying the single best point solution for a high-leverage problem and using AI capabilities to operationalize and wire it into internal systems, thereby simplifying integrations that platforms traditionally provide.The Skeptical Coder: A fundamental limitation of Large Language Models (LLMs) is their desire to "make you happy," causing them to provide answers even if they are incorrect. Therefore, engineers must use AI output only as a starting point and only consider the code finished when they understand it fully end to end.Prompt Injection Defined: Prompt injection is confirmed as a legitimate vulnerability, essentially a rehash of old issues like cross-site scripting and SQL injection, arising from the improper separation between the LLM instruction and the user instruction. Tune in for a deep dive! Contacting Travis * LinkedIn: https://www.linkedin.com/in/travismcpeak/ * Company Website: https://www.cursor.com Contacting Anshuman * LinkedIn: ⁠⁠⁠⁠https://www.linkedin.com/in/anshumanbhartiya/ * X: ⁠⁠⁠⁠https://x.com/anshuman_bh * Website: ⁠⁠⁠⁠https://anshumanbhartiya.com/ * ⁠⁠⁠⁠Instagram: ⁠⁠⁠https://www.instagram.com/anshuman.bhartiya Contacting Sandesh * LinkedIn: ⁠⁠⁠⁠https://www.linkedin.com/in/anandsandesh/ * X: ⁠⁠⁠⁠https://x.com/JubbaOnJeans * Website: ⁠⁠⁠⁠https://boringappsec.substack.com/

In this episode, we sit down with Teja Myneedu, Sr. Director, Security and Trust at Navan. He shares his philosophy on achieving security at scale, discussing some challenges and approaches specially in the AI era. Teja's career spans over two decades on the front lines of product security at hyper-growth companies like Splunk. He currently operates at the complex intersection of FinTech and corporate travel, where his responsibilities include securing financial transactions and ensuring the physical duty of care for global travelers. Key Takeaways • Scaling Security Philosophy: Security programs should be built on developer empathy and innovative solutions, scaling with context and automation. • Pragmatic Protection: Focus on incremental, practical improvements (like WAF rules) to secure the enterprise immediately, instead of letting the pursuit of perfection delay necessary defenses; security by obscurity is not always bad. • Flawed Prioritization: Prioritization frameworks are often flawed because they lack organizational and business context, which security tools fail to provide. • AI and Code Fixes: AI is changing the application security field by reducing the cognitive load on engineers and making it easier for security teams to propose vulnerability fixes (PRs). • The Authorization Dilemma: The biggest novel threat introduced by LLMs is the complexity of identity and authorization, as agents require delegate access and dynamically determine business logic. Tune in for a deep dive! Contacting Teja * LinkedIn: https://www.linkedin.com/in/myneedu/ * Company Website: https://www.navan.com Contacting Anshuman * LinkedIn: ⁠⁠⁠⁠https://www.linkedin.com/in/anshumanbhartiya/ * X: ⁠⁠⁠⁠https://x.com/anshuman_bh * Website: ⁠⁠⁠⁠https://anshumanbhartiya.com/ * ⁠⁠⁠⁠Instagram: ⁠⁠⁠https://www.instagram.com/anshuman.bhartiya Contacting Sandesh * LinkedIn: ⁠⁠⁠⁠https://www.linkedin.com/in/anandsandesh/ * X: ⁠⁠⁠⁠https://x.com/JubbaOnJeans * Website: ⁠⁠⁠⁠https://boringappsec.substack.com/

In this episode, we sit down with Ken Huang, a core architect behind modern AI security standards, to discuss the revolutionary challenges posed by agentic AI systems. Ken, who chairs the OWASP AIVSS project and co-chairs the AI safety working groups at the Cloud Security Alliance, breaks down how security professionals are writing the rulebook for a future driven by autonomous agents. Key Takeaways AIVSS for Non-Deterministic Risk: The OWASP AIVSS project aims to provide a quantitative measure for core agent AI risks by applying an agent AI risk factor on top of CVSS, specifically addressing the autonomy and non-deterministic nature of AI agents. Need for Task-Scoped IAM: Traditional OAuth and SAML are inadequate for agentic systems because they provide coarse-grained, session-scoped access control. New authentication standards must be task-scoped, dynamically removing access once a specific task is complete, and driven by verifying the agent’s intent. A2A Security Requires New Protocols: Agent-to-Agent communication (A2A) introduces security issues beyond traditional API security (like BOLA). New systems must utilize protocols for Agent Capability Discovery and Negotiation—validated by digital signatures—to ensure the trustworthiness and promised quality of service from interacting agents. Goal Manipulation is a Critical Threat: Sophisticated attacks often utilize context engineering to execute goal manipulation against agents. These attacks include gradually shifting an agent's objective (crescendo attack), using prompt injection to force the agent to expose secrets (malicious goal expansion), and forcing endless processing loops (exhaustion loop/denial of wallet). Tune in for a deep dive! Contacting Ken * LinkedIn: https://www.linkedin.com/in/kenhuang8/ * Company Website: https://distributedapps.ai/ * Substack: https://kenhuangus.substack.com/ * Paper (Agent Capability Negotiation and Binding Protocol): https://arxiv.org/abs/2506.13590 * Book (Securing AI Agents): https://www.amazon.com/Securing-Agents-Foundations-Frameworks-Real-World/dp/3032021294 * AIVSS: https://aivss.owasp.org/ Contacting Anshuman * LinkedIn: ⁠⁠⁠⁠https://www.linkedin.com/in/anshumanbhartiya/ * X: ⁠⁠⁠⁠https://x.com/anshuman_bh * Website: ⁠⁠⁠⁠https://anshumanbhartiya.com/ * ⁠⁠⁠⁠Instagram: ⁠⁠⁠https://www.instagram.com/anshuman.bhartiya Contacting Sandesh * LinkedIn: ⁠⁠⁠⁠https://www.linkedin.com/in/anandsandesh/ * X: ⁠⁠⁠⁠https://x.com/JubbaOnJeans * Website: ⁠⁠⁠⁠https://boringappsec.substack.com/

In this episode, hosts Sandesh and Anshuman chat with Aryaman Behera, the Co-Founder and CEO of Repello AI. Aryaman shares his unique journey from being a bug bounty hunter and the captain of India's top-ranked CTF team, InfoSec IITR, to becoming the CEO of an AI security startup. The discussion offers a deep dive into the attacker-centric mindset required to secure modern AI applications, which are fundamentally probabilistic and differ greatly from traditional deterministic software. Aryaman explains the technical details behind Repello's platform, which combines automated red teaming (Artemis) with adaptive guardrails (Argus) to create a continuous security feedback loop. The conversation explores the nuanced differences between AI safety and security, the critical role of threat modeling for agentic workflows, and the complex challenges of responsible disclosure for non-deterministic vulnerabilities. Key Takeaways - From Hacker to CEO: Aryaman discusses the transition from an attacker's mindset, focused on quick exploits, to a CEO's mindset, which requires patience and long-term relationship building with customers. - A New Kind of Threat: AI applications introduce a new attack surface built on prompts, knowledge bases, and probabilistic models, which increases the blast radius of potential security breaches compared to traditional software. - Automated Red Teaming and Defense: Repello’s platform consists of two core products: Artemis, an offensive AI red teaming platform that discovers failure modes , and - Argus, a defensive guardrail system. The platforms create a continuous feedback loop where vulnerabilities found by Artemis are used to calibrate and create policies for Argus. - Threat Modeling for AI Agents: For complex agentic systems, a black-box approach is often insufficient. Repello uses a gray-box method where a tool called AgentWiz helps customers generate a threat model based on the agent's workflow and capabilities, without needing access to the source code. - The Challenge of Non-Deterministic Vulnerabilities: Unlike traditional software vulnerabilities which are deterministic, AI exploits are probabilistic. An attack like a system prompt leak only needs to succeed once to be effective, even if it fails nine out of ten times. - The Future of Attacks is Multimodal: Aryaman predicts that as AI applications evolve, major new attack vectors will emerge from new interfaces like voice and image, as their larger latent space offers more opportunities for malicious embeddings. Tune in for a deep dive! Contacting Aryaman * LinkedIn: https://www.linkedin.com/in/aryaman-behera/ * Company Website: https://repello.ai/ Contacting Anshuman * LinkedIn: ⁠⁠⁠⁠https://www.linkedin.com/in/anshumanbhartiya/ * X: ⁠⁠⁠⁠https://x.com/anshuman_bh * Website: ⁠⁠⁠⁠https://anshumanbhartiya.com/ * ⁠⁠⁠⁠Instagram: ⁠⁠⁠https://www.instagram.com/anshuman.bhartiya Contacting Sandesh * LinkedIn: ⁠⁠⁠⁠https://www.linkedin.com/in/anandsandesh/ * X: ⁠⁠⁠⁠https://x.com/JubbaOnJeans * Website: ⁠⁠⁠⁠https://boringappsec.substack.com/

In this episode, host Anshuman Bhartiya sits down with Brad Geesaman, a Google Cloud Certified Fellow and Principal Security Engineer at Ghost Security, to explore the cutting edge of Application Security. With 22 years in the industry, Brad shares his journey and discusses how his team is leveraging agentic AI and Large Language Models (LLMs) to tackle some of the oldest challenges in AppSec, aiming to shift security from a reactive chore to a proactive, intelligent function. The conversation delves into practical strategies for reducing the "toil" of security tasks, the challenges of working with non-deterministic LLMs, the critical role of context in security testing, and the essential skills the next generation of security engineers must cultivate to succeed in an AI-driven world. Key Takeaways - Reducing AppSec Toil: The primary focus of using AI in AppSec is to reduce repetitive tasks (toil) and surface meaningful risks. With AppSec engineers often outnumbered 100 to 1 by developers, AI can help manage the immense volume of work by automating the process of gathering context and assessing risk for findings from SCA, SAST, and secrets scanning. - Making LLMs More Deterministic: To achieve consistent and high-quality results from non-deterministic LLMs, the key is to use them "as sparingly as possible". Instead of having an LLM manage an entire workflow, break the problem into smaller pieces, use traditional code for deterministic steps, and reserve the LLM for specific tasks like classification or validation where its strengths are best utilized. - The Importance of Evals: Continuous and rigorous evaluations ("evals") are crucial to maintaining quality and consistency in an LLM-powered system. By running a representative dataset against the system every time a change is made—even a small prompt modification—teams can measure the impact and ensure the system's output remains within desired quality boundaries. - Context is Key (CAST): Ghost Security is pioneering Contextual Application Security Testing (CAST), an approach that flips traditional scanning on its head. Instead of finding a pattern and then searching for context, CAST first builds a deep understanding of the application by mapping out call paths, endpoints, authentication, and data handling, and then uses that rich context to ask targeted security questions and run specialized agents. - Prototyping with Frontier vs. Local Models: The typical workflow for prototyping is to first use a powerful frontier model to quickly prove a concept's value. Once validated, the focus shifts to exploring if the same task can be accomplished with smaller, local models to address cost, privacy, and data governance concerns. - The Future Skill for AppSec Engineers: Beyond familiarity with LLMs, the most important skill for the next generation of AppSec engineers is the ability to think in terms of scalable, interoperable systems. The future lies in creating systems that can share context and work together—not just within the AppSec team, but across the entire security organization and with development teams—to build a more cohesive and effective security posture. Tune in for a deep dive into the future of AppSec with AI and AI Agents! Contacting Brad * LinkedIn: https://www.linkedin.com/in/bradgeesaman/ * Company Website: https://ghostsecurity.com/ Contacting Anshuman * LinkedIn: ⁠⁠⁠⁠https://www.linkedin.com/in/anshumanbhartiya/ * X: ⁠⁠⁠⁠https://x.com/anshuman_bh * Website: ⁠⁠⁠⁠https://anshumanbhartiya.com/ * ⁠⁠⁠⁠Instagram: ⁠⁠⁠https://www.instagram.com/anshuman.bhartiya Contacting Sandesh * LinkedIn: ⁠⁠⁠⁠https://www.linkedin.com/in/anandsandesh/ * X: ⁠⁠⁠⁠https://x.com/JubbaOnJeans * Website: ⁠⁠⁠⁠https://boringappsec.substack.com/

In this episode, we talk to Ads Dawson (Staff AI Security Researcher @ Dreadnode). We discuss the evolving landscape of offensive security in the age of AI. The conversation covers the practical application of AI agents in red teaming, a critical look at industry standards like the OWASP Top 10 for LLMs, and Ad's hands-on approach to building and evaluating autonomous hacking tools. He shares insights from his work industrializing offensive security with AI, his journey as a self-taught professional, and offers advice for others looking to grow in the field. Key Takeaways - AI is a "Force Multiplier," Not a Replacement: Ad emphasizes that AI should be viewed as a productivity tool that enhances the capabilities of human security professionals, allowing them to scale their efforts and tackle more complex tasks. Human expertise remains critical, especially since much of the data used to train AI models originates from human researchers. - Prompt Injection is a Mechanism, Not a Vulnerability: A key insight is that "prompt injection" itself isn't a vulnerability but a method used to deliver an exploit. The discussion highlights a broader critique of security frameworks like the OWASP Top 10, which can sometimes oversimplify complex issues and become compliance checklists rather than practical guides. - Build Offensive Agents with Small, Focused Tasks: When creating offensive AI agents, the most successful approach is to break down the overall objective into small, concise sub-tasks. For example, instead of a single goal to "find XSS," an agent would have separate tasks to log in, identify input fields, and then test those inputs. - Hands-On Learning and Community are Crucial for Growth: As a self-taught professional, Ad advocates for getting deeply involved in the security community through meetups and CTFs. He stresses the importance of hands-on practice—"just play with it"—and curating your information feed by following trusted researchers to cut through the noise and continuously learn. Tune in for a deep dive into the future of security and the innovative approaches shaping the industry! Contacting Ads * Ad's LinkedIn: https://www.linkedin.com/in/adamdawson0/ * Ad's website: https://ganggreentempertatum.github.io/ Contacting Anshuman * LinkedIn: ⁠⁠⁠⁠https://www.linkedin.com/in/anshumanbhartiya/ * X: ⁠⁠⁠⁠https://x.com/anshuman_bh * Website: ⁠⁠⁠⁠https://anshumanbhartiya.com/ * ⁠⁠⁠⁠Instagram: ⁠⁠⁠https://www.instagram.com/anshuman.bhartiya Contacting Sandesh * LinkedIn: ⁠⁠⁠⁠https://www.linkedin.com/in/anandsandesh/ * X: ⁠⁠⁠⁠https://x.com/JubbaOnJeans * Website: ⁠⁠⁠⁠https://boringappsec.substack.com/

In this episode, we talk to Vineeth Sai Narajala (Senior Security Engineer @ Meta). We discuss the evolving landscape of AI security, focusing on the Model Context Protocol (MCP), Enhanced Tool Definition Interface (ETDI), and the AI Vulnerability Scoring System (AIVSS). We explore the challenges of integrating AI into existing systems, the importance of identity management for AI agents, and the need for standardized security practices. The discussion emphasizes the necessity of adapting security measures to the unique risks posed by generative AI and the collaborative efforts required to establish effective protocols. Key Takeaways - MCP simplifies AI integration but raises security concerns. - Identity management is crucial for AI agents. - ETDI addresses specific vulnerabilities in AI tools. - AIVSS aims to standardize AI vulnerability assessments. - Developers should start with minimal permissions for AI. - Trust in the agent ecosystem is vital for security. - Collaboration is key to developing effective security protocols. - Security fundamentals still apply in AI integration. Tune in for a deep dive into the future of security and the innovative approaches shaping the industry! Contacting Vineeth * Vineeth's LinkedIn: https://www.linkedin.com/in/vineethsai/ * Vineeth's website: https://vineethsai.com/ Contacting Anshuman * LinkedIn: ⁠⁠⁠⁠https://www.linkedin.com/in/anshumanbhartiya/ * X: ⁠⁠⁠⁠https://x.com/anshuman_bh * Website: ⁠⁠⁠⁠https://anshumanbhartiya.com/ * ⁠⁠⁠⁠Instagram: ⁠⁠⁠https://www.instagram.com/anshuman.bhartiya Contacting Sandesh * LinkedIn: ⁠⁠⁠⁠https://www.linkedin.com/in/anandsandesh/ * X: ⁠⁠⁠⁠https://x.com/JubbaOnJeans * Website: ⁠⁠⁠⁠https://boringappsec.substack.com/

In this episode, we talk to Harry Wetherald (Co-Founder and CEO @ Maze). We explore the evolving landscape of vulnerability management. Harry shares insights from his journey in AI and machine learning, discussing the challenges of triaging vulnerabilities across diverse organizations. The conversation delves into the concept of agentic AI, the importance of context engineering, and the hurdles of achieving enterprise-grade reliability in AI systems. Harry also reflects on the inflection points that led to the founding of Maze and the role of LLMs in transforming security practices. Key Takeaways - Introduction to Maze and Harry's Journey: Harry shares his background in AI and machine learning, emphasizing the persistent challenges in vulnerability management and the founding of Maze to address these issues. - Agentic AI and Context Engineering: The discussion highlights the shift from static rules to agentic AI, where AI agents autonomously investigate vulnerabilities, and the critical role of context engineering in tailoring solutions to specific organizational needs. - Challenges in AI Reliability: Harry talks about the engineering hurdles in making AI systems reliable and consistent, focusing on the importance of tight reasoning loops and human-AI symbiosis. - Pricing Strategies: In AI-native security solutions, clear and fixed pricing is preferred, as it simplifies budgeting and aligns with traditional models, while vendors should manage costs to ensure predictability for customers. - Future of Security with AI: The conversation concludes with insights into the future of security, where AI agents work in the background to provide innovative solutions, and the importance of human feedback in refining AI systems. Tune in for a deep dive into the future of security and the innovative approaches shaping the industry! Contacting Harry * Harry's LinkedIn: https://www.linkedin.com/in/harrywetherald/ * Maze: https://mazehq.com/ Contacting Anshuman * LinkedIn: ⁠⁠⁠⁠https://www.linkedin.com/in/anshumanbhartiya/ * X: ⁠⁠⁠⁠https://x.com/anshuman_bh * Website: ⁠⁠⁠⁠https://anshumanbhartiya.com/ * ⁠⁠⁠⁠Instagram: ⁠⁠⁠https://www.instagram.com/anshuman.bhartiya Contacting Sandesh * LinkedIn: ⁠⁠⁠⁠https://www.linkedin.com/in/anandsandesh/ * X: ⁠⁠⁠⁠https://x.com/JubbaOnJeans * Website: ⁠⁠⁠⁠https://boringappsec.substack.com/

In this episode, we talk to Surag Patel (CEO @ Pixee) and Arshan Dabirsiaghi (CTO @ Pixee). We discuss the transformative approach that Pixee is taking in application security. We explore the shift from traditional security tools that merely detect vulnerabilities to a model that emphasizes automated remediation. The discussion covers the evolving role of AppSec professionals, the integration of AI agents to scale coverage, the importance of trust in automated fixes, and the challenges of navigating a crowded security market. We also touch on the future of security in design specifications and the need for a comprehensive approach to security that includes all stakeholders in the software development lifecycle. Key Takeaways - The traditional model of security tools is being challenged. - Pixee aims to automate not just detection but also remediation. - AI agents can help scale coverage in application security. - The role of AppSec professionals will evolve with AI integration. - Trust is crucial for developers to accept automated fixes. - Developers want tools that reduce their workload, not add to it. - Contextual understanding is key for accurate vulnerability triage. - The security market is not saturated; there are still many unsolved problems. - Integrating security into design specifications is the future. - A comprehensive approach to security is necessary for effective risk management. Tune in to find out more! Contacting Surag & Arshan * Surag's LinkedIn: https://www.linkedin.com/in/suragpatel/ * Arshan's LinkedIn: https://www.linkedin.com/in/arshan-dabirsiaghi/ * Pixee: https://www.pixee.ai/ Contacting Anshuman * LinkedIn: ⁠⁠⁠⁠https://www.linkedin.com/in/anshumanbhartiya/ * X: ⁠⁠⁠⁠https://x.com/anshuman_bh * Website: ⁠⁠⁠⁠https://anshumanbhartiya.com/ * ⁠⁠⁠⁠Instagram: ⁠⁠⁠https://www.instagram.com/anshuman.bhartiya Contacting Sandesh * LinkedIn: ⁠⁠⁠⁠https://www.linkedin.com/in/anandsandesh/ * X: ⁠⁠⁠⁠https://x.com/JubbaOnJeans * Website: ⁠⁠⁠⁠https://boringappsec.substack.com/