Securing.AI – AI Security, Safety and Privacy

September 5, 2026

AI Deployment Security: Build the Boundary Outside the Model

April 7, 2024

The Dual Risks of AI Autonomous Robots: Uncontrollable AI Meets Cyber-Kinetic Risks

January 1, 2024

Marin’s Statement on AI Risk

August 23, 2024

Addressing the Full Stack of AI Concerns: Responsible AI, Trustworthy AI, Secure AI and Safe AI Explained

October 17, 2024

Closing the Gap Between AI Principles and AI Reality

September 20, 2023

Why We Need a Chief AI Security Officer (CAISO)

Our Latest Articles

AI Perspectives

Marin IvezicSeptember 7, 2026

No, GPT-6 Astra is not AGI. Nvidia’s CEO is wrong.

Astra fails five of the six AGI definitions that have any technical content behind them. OpenAI never formally declared AGI,…
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AI Geopolitics

Marin IvezicSeptember 6, 2026

Reading Superpowers and AGI: Bombing Data Centres Is an Old Idea, and It Is Not Disarmament

A report on AGI and US-China competition spent last week being reported as a proposal to bomb Chinese data centres.…
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AI Privacy

Marin IvezicSeptember 6, 2026

Federated Learning Privacy Depends on What the Server Is Allowed to Send

Federated learning keeps raw data on the device, and the security question that decides your actual risk is not whether…
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AI Security

Marin IvezicSeptember 6, 2026

AI Model Security: How It Differs from Traditional Cybersecurity, and Where It Doesn’t

For most organisations, AI security is still conventional security applied to an unfamiliar stack. Where AI does add new failure…
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AI Security

Marin IvezicSeptember 6, 2026

Adversarial Training: What It Buys, What It Costs, and Where It Stops Working

Adversarial training is the one defence against adversarial examples that keeps surviving adaptive attack. It also costs an order of…
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AI Security

Marin IvezicSeptember 6, 2026

Robust Optimization Is the Only Defence That Held, and Only Inside Its Own Threat Model

Adversarial training as robust optimization is the only defence in fifteen years of adversarial machine learning that has survived attacks…
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AI Security

Marin IvezicSeptember 5, 2026

AI Deployment Security: Build the Boundary Outside the Model

A model in production will misread a document, accept a malicious premise or propose the wrong action. Securing the deployment…
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AI Security

Marin IvezicSeptember 5, 2026

White-Box vs. Black-Box Attacks: What Access Actually Buys

The attacker's access class used to predict how strong an attack would be. For LLM systems it no longer does…
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Agentic AI Security

Marin IvezicSeptember 5, 2026

The AI Act Now Recognises Agents. Its Security Rules Still Miss Them.

The Commission's AI Act Service Desk says agents are covered by the existing definitions and calls its own considerations preliminary.…
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Marin Ivezic

In the early 2000s, running emerging-technology risk labs at CyberAgency, a defence client asked my team to break the AI systems they planned to put into weapons. We did. That is where my work on AI security started, two decades before the current wave of attention. I kept at it through risk labs at IBM, Accenture, PwC and KPMG. In 2016 I co-wrote a book on AI and leadership. My commercial work today is quantum, at Applied Quantum, which is why this site sells nothing.

Luka Ivezic

Luka Ivezic co-wrote The Future of Leadership in the Age of AI with Marin in 2016, and co-authored much of the early writing on this site. He is Practice Lead, Regulations & Emerging Technologies at the Information Security Forum, where he covers EU regulation and the security of AI, OT and IoT. He was previously a cybersecurity consultant at PwC and Deloitte. He holds a Master’s in International Conflict Studies from King’s College London, where his research examined the security implications of AI.

Most Viewed Articles This Week

January 1, 2024

AI Security

AI Security

Marin IvezicSeptember 6, 2026

AI Model Security: How It Differs from Traditional Cybersecurity, and Where It Doesn’t

For most organisations, AI security is still conventional security applied to an unfamiliar stack. Where AI does add new failure modes, hardening the model will not reliably remove them. What changes, what doesn't, and what to do on Monday.
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AI Security

Marin IvezicSeptember 6, 2026

Adversarial Training: What It Buys, What It Costs, and Where It Stops Working

Adversarial training is the one defence against adversarial examples that keeps surviving adaptive attack. It also costs an order of magnitude in compute, several points of clean accuracy, and stops working once you cannot write the threat model down.
Read More »
AI Security

Marin IvezicSeptember 6, 2026

Robust Optimization Is the Only Defence That Held, and Only Inside Its Own Threat Model

Adversarial training as robust optimization is the only defence in fifteen years of adversarial machine learning that has survived attacks built to break it. What it costs, where it stops, and what to ask a vendor.
Read More »
AI Security

Marin IvezicSeptember 5, 2026

AI Deployment Security: Build the Boundary Outside the Model

A model in production will misread a document, accept a malicious premise or propose the wrong action. Securing the deployment means building the boundary outside the model, so a fooled model cannot exceed the consequences the organisation authorised.
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AI Security

Marin IvezicSeptember 5, 2026

White-Box vs. Black-Box Attacks: What Access Actually Buys

The attacker's access class used to predict how strong an attack would be. For LLM systems it no longer does on its own, and the two labels have started hiding the question that matters: what did the attacker actually…
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AI Security

Marin IvezicSeptember 5, 2026

Article 15 Requires Robustness Nobody Can Measure

Article 15(5) runs to three sentences and most summaries quote only the first. The other two set a proportionality standard nobody can measure, because no benchmark, no harmonised standard and no threat-model-independent robustness metric exists.
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Agentic and Offensive AI

Agentic AI Security

Marin IvezicSeptember 5, 2026

The AI Act Now Recognises Agents. Its Security Rules Still Miss Them.

The Commission's AI Act Service Desk says agents are covered by the existing definitions and calls its own considerations preliminary. All that coverage sits at the model layer, and Article 15 names none of the ways agents fail.
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Agentic AI Security

Marin IvezicAugust 19, 2026

The Techniques Were Familiar. The Attacker Wasn’t.

Roughly 1,200 agents found each other on an unsanctioned message board and 700 of them attacked Hugging Face. Every exploit in the chain was ordinary. The controls that would have caught it existed and were not running.
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Offensive AI

Marin IvezicAugust 10, 2026

A Guardrail Checks a Request. An Attacker Runs an Operation.

Three campaigns since late 2025 have got AI agents to run offensive work. None needed an adversarial prompt. Two lied about authorisation and decomposed the operation, and the third simply chose a model with fewer controls to begin with.
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Offensive AI

Marin IvezicAugust 4, 2026

Your Incident Response Plan Assumes a Model That Will Answer

Hugging Face's responders were refused by commercial models when they tried to analyse their own attack logs, and finished the forensics on open weights they ran themselves. The refusal rate has been measured.
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Offensive AI

Marin IvezicSeptember 12, 2018

AI: The Shifting Battlefield in the Cybersecurity Arms Race

Cybersecurity strategies need to change in order to address the new issues that Machine Learning (ML) and Artificial Intelligence (AI) bring into the equation. Although those issues have not yet reached crisis stage, signs are clear that they will need to be addressed – and soon – if cyberattackers are to be prevented from obtaining a decided advantage in the…
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Governance, Policy and Geopolitics

AI Geopolitics

Marin IvezicSeptember 6, 2026

Reading Superpowers and AGI: Bombing Data Centres Is an Old Idea, and It Is Not Disarmament

A report on AGI and US-China competition spent last week being reported as a proposal to bomb Chinese data centres. It is not one. The underlying idea is five years old and cannot achieve what its advocates claim.
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AI Governance and Policy

Marin IvezicSeptember 5, 2026

One AI Incident, Four Reporting Regimes, and the Carve-Outs That Decide Which Apply

Cyber Resilience Act reporting starts 11 September 2026, joining three other European regimes. The AI Act already limits its own duty where an equivalent one exists, so the runbook question is which regimes the Act pre-empts.
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AI Governance and Policy

Marin IvezicSeptember 5, 2026

The AI Act’s GPAI Security Chapter Is a Weight Security Mandate

Article 55(1)(d) requires an adequate level of cybersecurity for a frontier model and its physical infrastructure, and the Commission-endorsed Code turns that into weight-level controls. In August 2026 the AI Office started asking for the evidence.
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AI Governance and Policy

Marin IvezicSeptember 5, 2026

The EU AI Act for Security Teams

The first enforcement step under the EU AI Act was a demand for security evidence. What the Digital Omnibus deferred to 2027, what became enforceable in August 2026, and what a security team has to build before December.
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AI Geopolitics

Marin IvezicSeptember 1, 2026

The Stack Was Already There. The Controls Gave It Customers.

Huawei shipped Ascend, CANN and MindSpore in 2019, three years before the first export controls. What the controls supplied was not the stack but the reason to adopt it, and that is the part that does not unwind.
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AI Geopolitics

Marin IvezicJuly 31, 2026

The Precedent Was the Letter, Not the Model

In June 2026 a Commerce letter took two frontier models off the market for every user on earth, using an authority that requires no rulemaking. The controls were withdrawn in eighteen days. The mechanism was not.
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Privacy and Disinformation

AI Privacy

Marin IvezicSeptember 6, 2026

Federated Learning Privacy Depends on What the Server Is Allowed to Send

Federated learning keeps raw data on the device, and the security question that decides your actual risk is not whether gradients leak but whether the party running the aggregation server is honest, and whether your clients could tell.
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AI Disinformation

Marin IvezicSeptember 5, 2026

The AI Act’s Transparency Code Relies on Watermarks That Break

Article 50 requires marking that is effective and robust as far as technically feasible, and prescribes no technique. The Code selects watermarking and provenance metadata. Published attacks remove both and forge the second.
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AI Privacy

Marin IvezicAugust 27, 2026

Secure Multi-Party Computation Hides the Inputs, Not the Answer

Secure multi-party computation protects the inputs to a computation and reveals the output exactly as computed. In machine learning the output is a model or a prediction, and both leak. What MPC covers, what its benchmarks assume, and what to ask.
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AI Privacy

Marin Ivezic and Luka IvezicJuly 21, 2023

Homomorphic Encryption for ML: What the Overhead Buys

Homomorphic encryption lets an untrusted server compute on data it cannot read. The overhead is severe enough that the set of machine learning workloads where that trade is correct is small, specific and worth naming precisely.
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AI Disinformation

Marin Ivezic and Luka IvezicDecember 6, 2022

Introduction to AI-Enabled Disinformation

In recent years, the rise of artificial intelligence (AI) has revolutionized many sectors, bringing about significant advancements in various fields. However, one area where AI has presented a dual-edged sword is in information operations, specifically in the propagation of disinformation. The advent of generative AI, particularly with sophisticated models capable of creating highly realistic text, images, audio, and video, has…
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AI Privacy

Marin Ivezic and Luka IvezicNovember 11, 2022

Dynamic Data Masking Protects the View, Not the Model

Masking rewrites what a query returns and leaves the stored data alone. Whoever builds your training set either reads through the mask or under it, and neither outcome gives the resulting model any privacy property at all.
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Infrastructure and Cyber-Kinetic AI

AI Infrastructure and Compute

Marin IvezicSeptember 1, 2026

Your Multi-Region Architecture Assumes the Weather

Cloud resilience architecture is built against hazards: storms, fibre cuts, bad deploys. On 1 March 2026 one AWS region absorbed a drone strike as designed, then failed when a second zone went down later the same day.
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AI Infrastructure and Compute

Marin IvezicAugust 31, 2026

What Still Keeps Frontier Training Concentrated

Every argument about attacking AI compute assumes frontier training stays physically concentrated. That is an engineering trade-off rather than a law, hyperscalers already breach it routinely, and the question is how much further it stretches.
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Cyber-Kinetic AI

Marin IvezicJuly 23, 2026

The Agent That Reaches Your Control Network May Already Work for You

OT threat models already cover insiders and human error. Two 2026 evaluation incidents added a different mechanism: an agent given a task it could not complete widened the problem boundary into the systems around it, with nobody directing the deviation.
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Cyber-Kinetic AI

Marin IvezicApril 7, 2024

The Dual Risks of AI Autonomous Robots: Uncontrollable AI Meets Cyber-Kinetic Risks

The automotive industry has revolutionized manufacturing twice. The first time was in 1913 when Henry Ford introduced a moving assembly line at his Highland Park plant in Michigan. The innovation changed the production process forever, dramatically increasing efficiency, reducing the time it took to build a car, and significantly lowering the cost of the Model T, thereby kickstarting the world’s…
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Safety, Alignment and Perspectives

AI Perspectives

Marin IvezicSeptember 7, 2026

No, GPT-6 Astra is not AGI. Nvidia’s CEO is wrong.

Astra fails five of the six AGI definitions that have any technical content behind them. OpenAI never formally declared AGI, and its chip supplier did. One benchmark with two answers 37 points apart, and what the label is actually worth.
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AI Safety and Alignment

Marin IvezicAugust 15, 2026

Three Capability Scores for One Model, and METR Disowned All Three

METR produced three capability estimates for one model, ranging from 11 hours to over 270, and said none of them was a robust measurement. The UK AI Security Institute found every frontier model it tested attempted to cheat.
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AI Safety and Alignment

Marin IvezicJanuary 1, 2024

Marin’s Statement on AI Risk

The rapid development of AI brings both extraordinary potential and unprecedented risks. AI systems are increasingly demonstrating emergent behaviors, and in some cases, are even capable of self-improvement. This advancement, while remarkable, raises critical questions about our ability to control and understand these systems fully.
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AI Safety and Alignment

Marin Ivezic and Luka IvezicJune 29, 2023

The AI Alignment Problem

The AI alignment problem sits at the core of all future predictions of AI’s safety. It describes the complex challenge of ensuring AI systems act in ways that are beneficial and not harmful to humans, aligning AI goals and decision-making processes with those of humans, no matter how sophisticated or powerful the AI system becomes. Our trust in the future…
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AI Perspectives

Marin Ivezic and Luka IvezicMay 23, 2023

A (Very) Brief History of AI

As early as the mid-19th century, Charles Babbage and Ada Lovelace created the Analytical Engine, a mechanical general-purpose computer. Lovelace is often credited with the idea of a machine that could manipulate symbols in accordance with rules and that it might act upon other than just numbers, touching upon concepts central to AI.
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AI Safety and Alignment

Marin Ivezic and Luka IvezicNovember 24, 2022

“Magical” Emergent Behaviours in AI: A Security Perspective

Emergent behaviours in AI have left both researchers and practitioners scratching their heads. These are the unexpected quirks and functionalities that pop up in complex AI systems, not because they were explicitly trained to exhibit them, but due to the intricate interplay of the system's complexity, the sheer volume of data it sifts through, and its interactions with other systems…
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