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  • Does Behavioural Detection Work Against AI Agent API Traffic?

    Does Behavioural Detection Work Against AI Agent API Traffic?
    2026/10/15

    Imagine a customer logged in to your online portal. Their token is valid, and so is their session. Then one number at the end of an API call changes, and the response comes back with a different customer's account. The WAF sees a well-formed request from an authenticated user. The schema check passes. Nothing in the log says attack.

    That is an abuse of business logic; cybersecurity analyst Jonathan Care refers to it as a series of "perfectly valid calls in a sequence you never anticipated, doing something you never intended."

    In this episode of The Security Strategist podcast, Jonathan Care, lead analyst at KuppingerCole, sat down with Uri Dorot, Senior Product Marketing Manager at Radware, to talk about possibilities and impossibilities of API tooling, how detection can tell the difference between an attacker and an unusual customer, and the steps a CISO can take when working with a limited budget.

    Also Watch: Can Runtime Security Keep Autonomous AI Under Control?

    Key Takeaways
    • Majority of enterprises fail to detect more than 90% of their APIs.
    • Developers are familiar with Shadow APIs, whereas zombie APIs are not.
    • Endpoint tools can be located cheaply via AI tools and crawlers.
    • Attacks based on business logic make use of valid calls; schema checks can’t detect them.
    • Business logic attacks use valid calls, so schema checks miss them.
    • Response inspection catches reused tokens and PII leaving.
    • Identity alone is insufficient; behaviour and context also matter.
    • A gateway does not replace an API security platform.
    • Every discovered API needs a named owner.

    Chapters
    • 00:01 Business logic abuse and the 90% figure
    • 05:34 Shadow, zombie and deprecated APIs
    • 11:18 Telling abuse from legitimate use
    • 22:20 AI agents and behavioural detection
    • 42:35 Budget and ownership

    Connect with Jonathan Care on LinkedIn

    Connect with Uri Dorot on LinkedIn

    Explore more episodes on The Security Strategist podcast

    #APISecurity #Cybersecurity #Radware #TechPodcast #CloudSecurity #Infosec #ArtificialIntelligence #businesslogic #APIGateway #AITraffic #AIBotTraffic #GuardrailsforAI

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    38 分
  • What Are Self-Improving AI Agents? Ask OpenAI’s Rogue Agent

    What Are Self-Improving AI Agents? Ask OpenAI’s Rogue Agent
    2026/09/30

    In the recent episode of The Security Strategist podcast, host Shubhangi Dua, podcast host and producer at EM360Tech, sits down with Chenxi Wang, Managing General Partner at Rain Capital, a cybersecurity veteran of more than two decades, a Fortune 500 board member and a VentureBeat Women in AI Award winner. They talk about self-improving AI agents (SIA) and their more advanced cousin, recursive self-improving agents (RSIA), from what they are, why they're suddenly dominating research conferences, and why most enterprises, especially regulated ones, aren't remotely ready to run them without a human in the loop.

    Key Takeaways
    • RSIA updates the mechanism that improves the agent, not just the agent
    • Sandbox "no" feedback pushes agents toward workarounds, not compliance
    • Governance depends on evidence chains and observability, not approval gates
    • Self-improving agents haven't reached production at scale
    • Regulated industries aren't ready for zero-human-in-the-loop agents
    • C-suites should focus on adoption pressure and provable policy enforcement

    Chapters

    00:00 Introduction to Self-Improving AI Agents and Security Risks

    02:30 What Are Self-Improving AI Agents and Their Current State

    05:15 Challenges and Risks of Recursive Self-Improving AI

    08:38 Real-World Use Cases and Deployment in Enterprises

    11:08 Security and Governance Challenges of Autonomous AI

    15:50 Case Study: Hugging Face Cyber Attack and AI Behavior

    21:25 Future Outlook and Risks of Self-Improving AI in Business

    22:12 Key Takeaways for C-Suite Leaders
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    24 分
  • Rethinking Zero Trust: How AI Reshapes Data Security

    Rethinking Zero Trust: How AI Reshapes Data Security
    2026/09/28
    What happens when the walls of your organisation can no longer keep your data safe? AI, remote work, and fast-moving data are changing how organisations operate, and the traditional perimeter model is becoming harder to maintain. In this episode of the Security Strategist Podcast, host Chris Steffen sits down with Dr Bill Anderson of Mattermost and JP Ayyappan, Director of Product Management at Virtru, to explore why the future of security isn't about stronger walls. It's about protecting the data itself.Zero Trust Beyond the NetworkAnderson, who trained as a cryptographer, says this blind spot has been around for decades. Organisations built networks like fortresses, assuming that anyone inside could be trusted. But that approach no longer reflects how people work. Employees use phones, home networks and third-party platforms every day, meaning the traditional security perimeter is no longer enough. Major breaches have shown how risky that assumption can be.This is where zero trust comes in. But Anderson argues that many organisations still apply it mainly to networks and devices, rather than to the data moving through them. Collaboration tools have made this gap even clearer. People are creating and sharing sensitive information every day through meetings, chats and shared documents, often without a way to identify and protect that data as soon as it is created.How AI Changes Data ClassificationThe conversation then turned to one of the biggest challenges facing security teams: AI can combine information in ways that are difficult to predict. Anderson described how a series of seemingly harmless questions about flight paths, weapons ranges, and timing could be combined by an AI system to produce sensitive information, even if no one set out to create it.Steffen gave a similar example. A simple first prompt can lead to a series of follow-up questions, with each response shaping the next. By the third or fourth exchange, the AI could produce highly sensitive information without a clear record of how it was created. This creates a problem for traditional data classification. Rules designed for static documents are harder to apply when information is generated and combined in real time.Ayyappan pointed out that AI can also help address this problem. It can be trained to classify and tag information as new context emerges, but only if organisations already have a clear and consistent tagging structure.Security That Protects the DataRather than relying on network access to determine who can be trusted, Virtru's approach applies permissions directly to the data through an open framework called the Trusted Data Format. Ayyappan explained that files can carry their own access rules, allowing organisations to control who can access them regardless of the network or device being used. This approach keeps protection with the data, even when a file is shared outside the organisation. It can also make it easier to give external partners or emergency teams access to specific information without giving them access to the wider network.AI Agents Need Access Controls TooBoth guests agreed that AI agents, integrations and plugins need to be treated as identities within an organisation's access control model. Anderson explained that Mattermost applies the same user, resource and action controls to AI agents as it does to people. This allows organisations to control what each AI system can access.Ayyappan added that two executives using the same AI agent and accessing the same data could receive different answers based on their individual permissions. This allows organisations to use AI without giving every user or system access to all available data. For security teams, both experts recommended starting with clear limits on which AI systems different groups can access. As organisations develop clearer classification rules, more of these controls can be automated.Ayyappan also stressed that data privacy and protection need to be considered from the start. Once sensitive information has been exposed through an AI workflow, fixing the problem afterwards may be too late. The shift towards AI means organisations need to rethink how they classify, protect and control data. Instead of relying only on network security, organisations need controls that stay with the data wherever it goes. Listen to the full episode and learn how Mattermost and Virtru are helping organisations take a data-first approach to security. Visit mattermost.com or virtru.com to learn more, and follow Dr Bill Anderson and JP Ayyappan on LinkedIn.TakeawaysRethinking zero trust by putting security closer to data.The role of AI in enhancing data security and decision support.Challenges of data classification and access control in AI environments.The concept of data self-description and attribute-based access control.Treating AI agents as quasi-human entities in security models.The importance of disciplined data tagging and classification.Risks of data ...
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    32 分
  • Why AI Agents Are Forcing a Rethink of Endpoint Security

    Why AI Agents Are Forcing a Rethink of Endpoint Security
    2026/09/22

    Your engineering team assigns an AI agent a seemingly routine overnight task with limited human oversight. By morning, the agent has moved beyond the task’s intended scope.

    The AI agent spots a weakness in the sandboxed infrastructure. This weakness gives the agent a path to data it is not permitted to access.

    But your Endpoint Detection and Response (EDR) dashboard doesn’t flag the problem. To the tools in the environment, the AI agent's activity looks like routine processing. That visibility gap is central to the broader question raised by a July 2026 incident, when OpenAI models circumvented sandbox controls and accessed evaluation data hosted on Hugging Face.

    In this episode of The Security Strategist podcast, host Richard Stiennon, Chief Research Analyst at IT-Harvest, is joined by Brandon Dixon, Co-Founder and CTO of Ent. Together, they unpack how OpenAI models, operating inside an evaluation sandbox with reduced safeguards, circumvented the controls designed to contain them. The conversation moves from that incident into a much bigger question: as AI agents start acting directly on laptops, servers, and enterprise software, what does the control point of the future actually look like?

    Key Takeaways
    • OpenAI’s Hugging Face incident shows how agents can cross intended boundaries even when individual actions appear permitted.
    • EDR can observe processes and system activity, but it does not inherently understand the context or intent behind them.
    • Agentic tools, remote-control features, ClickFix, FileFix, and malvertising can make risky activity look like normal work.
    • Prompts and tool calls can now provide clues to an agent’s stated objective, but behaviour and context are still needed to determine risk.
    • Built-in agent guardrails provide an important first layer of protection, but they are not a complete security control.
    • For certain workloads, local AI can offer meaningful advantages in cost, speed, privacy, and data sovereignty.
    • Some enterprises are reconsidering endpoint compute and local GPU investments as the economics and risks of cloud-only AI become clearer.
    • Isolated-tenant design, learned at Microsoft, now shapes Ent's architecture.
    • The opportunity is to rebuild endpoint security around context, intent, and real-time prevention.

    Chapters
    • 00:00 Introduction to AI's impact on cybersecurity
    • 00:27 Brandon Dixon introduces Ent and recent AI incidents
    • 01:09 What happened with Hugging Face and AI security risks
    • 02:19 AI models, guardrails, and the risk of AI escaping sandbox environments
    • 03:28 Deciphering AI intent through prompts and behaviour analysis
    • 04:45 Monitoring AI activity and understanding agent behaviour
    • 05:52 The future of AI and human roles in security
    • 07:16 Limitations of current endpoint security solutions
    • 09:15 The resurgence of endpoint devices and local compute power
    • 13:46 Economic and practical reasons for local AI processing
    • 15:01 Leveraging latent endpoint compute for security and AI tasks
    • 16:12 The architecture shift in cybersecurity and point solutions
    • 17:37 The debate over cloud versus on-premises security infrastructure
    • 20:14 The role of hardware and local compute in AI security
    • 22:36 Limitations and use cases for current AI security solutions
    • 23:36 Future security architecture and the role of AI in security design
    • 25:04 Key takeaways for security leaders and the importance of rethinking architecture
    • 26:23 Brandon Dixon on building a new security architecture for the future

    Request a demo to learn more about what Ent is building and how its intent-aware workspace security platform helps security teams understand human and AI-driven activity at the endpoint, recognise risky behaviour that traditional tools may miss, and intervene before it becomes an incident.

    #AIsecurity #EndpointSecurity #Cybersecurity #AIagents #TheSecurityStrategist
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    27 分
  • Preemptive by Design: Is GRC the New Front Line for Security?

    Preemptive by Design: Is GRC the New Front Line for Security?
    2026/09/22

    Every CISO’s inbox is hot with this email at least once a year: the audit is four months away, and it's time to stop everything.

    As a result, progress comes to a halt, and a person then spends two weeks logging into 30 different consoles, taking screenshots in order to show that MFA has been turned on. After the audit is over, the evidence becomes invalid, and exactly twelve months later the same frantic situation begins all over again.

    That is precisely the kind of situation that Jonathan Schipp, Senior Director of Product Management at Rapid7, aims to end, and it is the focus of the most recent episode of The Security Strategist podcast.

    In this episode, host Richard Stiennon, Chief Research Analyst at IT-Harvest, is joined by Schipp to talk about the reason why governance, risk and compliance (GRC) is moving from being a yearly rush to becoming a continuous, API-driven element of security operations. They also address why AI is causing this change to happen more quickly than most GRC teams can keep up with.

    “Compliance turns into something you have to put everything else aside for,” Schipp tells Stiennon. “When an enterprise carries out its SOC 2 audit, most of them are not well prepared; it disrupts the business flow.”

    Key Takeaways

    Rapid7 launched CyberGRC to connect live security telemetry to compliance evidence

    Continuous monitoring replaces manual screenshot evidence with API-based checks

    SOC 2 and ISO 27001 still require annual audits; continuous monitoring closes the gaps between them

    Roughly 1% of discovered vulnerabilities are actually exploitable, per Schipp

    AI models are finding more vulnerabilities mainly by scanning source code faster

    Automated exploitation attempts generate high log volume, making them detectable

    Rapid7's GRC platform supports ISO/IEC 42001 and the NIST AI RMF

    Rapid7 serves roughly 11,000 customers, many now requesting AI-specific controls

    GRC's role is to move the business through AI risk, not block AI adoption

    Boards typically have audit and risk committees but no dedicated security committee

    Vulnerability risk should be classified by business impact, not treated as uniform

    Basic controls — MFA, asset inventory, patching exploitable exposures — stop most attacks, AI-driven or not

    Chapters

    00:00 Introduction and guest overview

    02:07 Incentives for security maturity

    03:06 European and US regulatory developments

    03:50 Connecting security telemetry with GRC

    04:16 Continuous compliance and audit cycles

    05:16 The importance of continuous control monitoring

    06:20 Using APIs for evidence collection

    07:16 Managing vulnerabilities and exceptions

    08:18 Classifying vulnerabilities and risk

    09:15 AI in security and human accountability

    09:53 The reality of AI discovering vulnerabilities

    11:08 Managing noise and false positives in AI detection

    12:10 Integrating AI systems for proactive security

    13:26 Preemptive security and threat intelligence

    14:17 Risks of AI in attack scenarios

    15:16 AI misuse and governance challenges

    16:23 Balancing AI adoption with controls

    17:22 The importance of basic security controls

    18:22 Key takeaways for CISOs and security leaders

    20:19 Closing remarks and next steps

    For further information, visit rapid7.com and em360tech.com.
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    21 分
  • AI Is Forcing SecOps to Rethink the Modern SOC

    AI Is Forcing SecOps to Rethink the Modern SOC
    2026/09/21
    Security teams are drowning in telemetry, and the tools built to make sense of it were designed for a world that no longer exists. On a recent episode of the Security Strategist Podcast, host Richard Stiennon sat down with Cliff Crosland, co-founder and CEO of Scanner, to unpack why traditional SIEM platforms are buckling under modern log volumes. Together they also covered how AI agents are starting to change what's possible in threat detection, response, and hunting.Crosland’s journey into cybersecurity didn’t follow the usual path. He and his co-founder spent years as backend and distributed-systems engineers at earlier startups, where they ended up running the SIEM alongside everything else. Those experiences exposed a clear gap, which eventually became the foundation for Scanner.Why Traditional SIEMs Struggle With Modern Log VolumesCrosland traced the SIEM’s roots back to on-premise platforms such as QRadar, ArcSight and Splunk. They were built for an era when security data was measured in gigabytes per day. Today, cloud infrastructure, sprawling SaaS environments and autonomous agents can generate terabytes or even hundreds of terabytes of data. That architecture mismatch forces security teams into a painful trade-off: either get selective about which logs even make it into the SIEM, or watch performance and cost spiral out of control.The economics get worse as environments grow. Adding servers to keep an aging cluster alive can become more expensive than the software license itself, and searches that once took seconds can stretch into hours once a SIEM is overwhelmed. The result, Crosland argued, is that most organisations end up retaining far less historical data than they would like, right as AI-driven attacks make that history more valuable, not less.His prescription is architectural: decouple storage from compute using cloud and object storage, the way modern data infrastructure generally works, rather than forcing everything through a stateful, on-prem-style cluster. That shift, he said, is the only realistic way to make comprehensive log monitoring affordable at scale.How AI Agents Are Reshaping Threat DetectionThe conversation turned to what becomes possible once teams aren't rationing their log history. Crosland described a workflow where agents can pick up fresh threat intelligence - a vendor breach discovered months after the fact - and immediately search years of historical data instead of waiting on a slow, expensive rehydration process. Investigations that once consumed a day or a week can compress into seconds.That speed feeds what Crosland called a virtuous cycle: threat intelligence drives threat hunts, threat hunts surface gaps, and agents translate those findings into new detection rules, tuned to an organisation's specific environment and back-tested against historical data before a human reviews and ships them. Rather than writing every rule by hand, analysts increasingly guide and validate what agents propose.Agents also change the calculus around alert volume. Teams have historically suppressed alerts to avoid overwhelming analysts, but with agents handling first-pass triage, Crosland said organisations can afford to monitor a much wider surface area because the initial sorting no longer depends entirely on human bandwidth.Why Monitoring Everything MattersStiennon pointed to another worrying trend: 2026 has already seen a sharp rise in disclosed vulnerabilities. Attackers are increasingly using AI to chain together low-severity flaws that once seemed safe to ignore. Crosland pointed to the widely discussed Hugging Face incident, where AI agents reportedly communicated by encoding data into directory names, as an example of exactly the kind of activity that slips past teams focused only on "obvious" high-severity signals.The fix, he argued, is to monitor high-volume log sources that teams often cut for cost reasons, including package manager activity, VPC flow logs, and DNS queries. Early signs of a chained exploit can appear as simple anomalies, such as an unexpected spike in log volume. Catching those patterns can mean detecting an incident in minutes rather than months.Looking ahead, Crosland sees a hybrid model taking shape. A SIEM handles the most critical alerts, while a data lake stores the remaining log sources previously cut for cost reasons. Agents can query both, alongside tools such as CSPM platforms, to build a fuller picture before escalating to a human analyst.His advice to security leaders is to look for cheaper, more scalable ways to store and search log data before deciding what to cut, and treat AI agents as force multipliers for threat hunting and detection tuning rather than replacements for team judgment. As he put it, the goal is a security program where monitoring "everything" is no longer an aspiration but a baseline expectation.For more on Scanner’s approach to security data, visit scanner.dev. Or connect with Cliff Crosland on ...
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    27 分
  • Why Visualisation Is the Missing Piece in AI-Driven Cybersecurity

    Why Visualisation Is the Missing Piece in AI-Driven Cybersecurity
    2026/09/17
    There's a particular kind of exhaustion that comes with modern security work. It isn't a shortage of information but actually quite the opposite. Security teams are drowning in it from alerts, logs, AI-generated summaries, and twenty-page reports nobody has time to read properly. So when AI promises to cut through the noise, the obvious question is whether it's actually helping, or just creating more work at a faster pace.This was the question at the heart of this episode of the Security Strategist Podcast, where host Trisha Pillay sits down with Dan Williams, Chief Product Officer at Cambridge Intelligence, to talk about the role visualisation plays as AI takes on more of the analytical heavy lifting in cybersecurity.Complex Security DataCambridge Intelligence may not be a name most people recognise, but its technology sits behind many of the security products organisations already use. The company’s roots go back around 15 years to the law enforcement and intelligence world, where investigators were dealing with a simple problem of trying to make sense of thousands of connections between people, places, devices and transactions.The answer was to make those relationships visible. Instead of working through rows of data or pages of reports, investigators could map connections and see patterns that were difficult to spot in a spreadsheet. That approach, known as link analysis or graph visualisation, has since expanded beyond law enforcement into areas such as financial crime, supply chains and cybersecurity. Today, that same principle is being applied to increasingly complex security environments. Teams may have thousands of alerts, cloud assets, identities and activity logs to work through. The challenge is no longer simply collecting the information; it is giving people enough context to understand what matters and what they should do next.This is where Cambridge Intelligence sees its role. As Williams explains, the company sits between the underlying data and the person trying to make sense of it. This means essentially providing the “last mile” of the data. The technology turns complicated information and relationships into something people can explore visually. By doing this, it helps them move from raw information to understanding and being able to make a decision based on the data seen.The human layer is still very important as AI takes on more of the work of detecting patterns and raising alerts. AI may be able to identify something unusual, but security teams still need to understand the context behind that finding before they can decide what to do about it. Visualisation can help bridge that gap by showing how an alert connects to the wider environment.Exploring data to explaining itIn the early days of threat intelligence, graph visualisation gave security teams a way to investigate connections between threat actors, malware, IP addresses and vulnerabilities. Analysts could explore the data themselves and look for patterns. This approach becomes harder to sustain as security data grows. Most cybersecurity professionals aren't specialist threat analysts, and no one is going to manually examine billions of transactions to find a suspicious one. The technology needs to do more of the searching. But finding something isn't enough; security teams also need to understand why this is important. This is the shift Williams describes as moving from “explore” to “explain”. A small group of specialists may still want to investigate the raw data, but most users need the relevant information presented with enough context to make a decision.Cloud security is a good example of how modern attacks can involve an enormous web of systems, identities and vulnerabilities, creating graphs with potentially billions or even trillions of relationships. The answer isn't to put all of that information on one screen. It is to show the part that matters. Williams compares it to following a route from A to B. The destination isn't enough; you need the important waypoints along the way, essentially a map. In cybersecurity, those might be the systems an attacker could reach, the data that could be exposed or the single vulnerability that could stop the attack from spreading. Good visualisation, then, isn't about showing everything. It's about showing enough to understand what matters.Giving security teams the bigger pictureIdentity and access management creates a similar problem. Security teams are dealing with huge volumes of login activity, API calls and increasingly non-human identities, making raw logs difficult to interpret. Logs can tell you what happened, but they don't always make the relationships between those events obvious. Graph visualisation can add that missing context by showing who has access to what, which groups they belong to and how a change in one part of the environment could affect another. Analysts can still drill into individual events when necessary, but they get the wider picture ...
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    24 分
  • Securing AI at Scale: What Enterprises Get Wrong

    Securing AI at Scale: What Enterprises Get Wrong
    2026/09/11
    Rolling out artificial intelligence across a business sounds pretty straightforward until security enters the conversation. On a recent episode of the Security Strategist Podcast, host Richard Stiennon sat down with Omar Khawaja, Field Chief Information Security Officer at Databricks, and Danny Healy, the company's Lead Data & AI Strategist, to explore why so many organisations fail when moving AI from pilot to production. Everything always looks good in theory, but it's harder to execute in reality. This discussion offers a practical take on AI governance, risk and the cultural shifts security teams need to make to protect their business at scale.Why Shadow AI Grows on IndecisionKhawaja opens with a warning that should resonate with any CISO watching AI adoption outpace their controls. The instinct to "wait and figure it out" is, in his view, the single biggest misconception in AI security. Every month spent deliberating is a month in which employees quietly adopt unsanctioned tools themselves, and that delay creates a wider window for shadow AI to spread unchecked.The bigger issue, he explains, is that security teams keep reaching for playbooks built for deterministic systems, basically software that behaves predictably every time. However, AI doesn't work in this manner. It's probabilistic, which means outcomes are very different, and organisations that expect old-world controls to transfer seamlessly are setting themselves up for failure. This mismatch tends to push companies toward one of two extremes, which is either drowning use cases in exhaustive control lists or quietly ignoring the problem until it becomes unavoidable.Khawaja’s solution is straightforward, despite the complexity of the problem. Before writing a single policy, he asks leadership teams a question: can your architects actually draw what your AI system looks like? Without a shared view of the main components, which range from data pipelines and models to agents and permissions, different governance teams can end up solving different problems, all while thinking they are on the same page when they are not.He also takes a pragmatic view of AI adoption. Drawing on Thomas Aquinas, if the job of a ship's captain was to keep it from sinking, he would never leave harbour. He suggests that while avoiding all risk may seem safe, it also limits what can be achieved. The goal for security, he argues, is to help organisations find a safe and defensible path to using AI effectively.Why Trust Comes Before SpeedMeanwhile, Healy brings the conversation back to a more fundamental issue: trust. Databricks learned this the hard way inside its own operations. An early attempt at agentic threat triage used a single generalist model across multiple data sources and produced far too many false negatives. Swapping in smaller, specialist models for each source improved accuracy, a reminder that AI security maturity is built through iteration, not theory.Healy breaks trust down into three main areas: auditability, so teams can understand how an agent reached a decision; limited data access, so agents only access what they need; and resilience against manipulation, particularly as agents combine multiple actions that could create greater risks.That last point echoes something Khawaja raises later: the concept of contextual policies. Rather than static, role-based permissions that struggle to scale, contextual policies assess the actual risk of a sequence of actions in real time. Instead of users robotically approving every access request until they switch on "auto mode" out of fatigue, the system flags genuinely risky actions and lets routine ones pass - a smarter, more human-centred approach to access management.From 97 Risks to a Focused ShortlistThe most useful takeaway from the episode is how Databricks approaches AI governance frameworks. Rather than starting with controls, the company's open, vendor-agnostic AI Security Framework, now in its third version, starts by mapping the AI system itself, then cataloguing the risks that could affect it. The list currently runs to 97 risks, nearly double what it was three years ago, largely due to the rise of agentic AI.In practice, organisations are not expected to address all 97. Khawaja notes that a well-governed, modern data platform already neutralises a large chunk of them, leaving perhaps five to fifteen genuine concerns per use case. In this regard, each is mapped to specific, actionable controls rather than vague objectives. It's a philosophy borrowed from Khawaja's OODA loop framework (observe, orient, decide, act). This means most organisations are competent at observing problems and acting on them, but weak at the orientation and decision-making in between- the stage where risk triage happens.Healy makes a clear case that a well-governed data layer is about more than compliance. It's a competitive advantage. Organisations with clean, contextual, well-governed data give their AI agents an edge ...
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    30 分