You find it's a tight regulation for a new artificial intelligence (AI) - No problem

 Building a new AI system? Don't wait for regulation to surprise you 🚀

The world is moving towards tight regulation of artificial intelligence, but the average entrepreneur or product manager finds himself facing a maze: the European EU AI Act, the US NIST standards and the OECD principles.

What's the difference and how do you stay relevant?

Basically, everyone agrees on the "what": human rights, fairness, transparency and safety. But the "how" is completely different: 🔹 OECD: Voluntary value framework ("Soft Law"). The moral compass that everyone started with. 🔹 EU: Mandatory regulation ("Hard Law") with teeth, risk classification and heavy fines. 🔹 US: A combination of voluntary guidelines and sectoral regulation (health, finance).

🗯️ My tip: Start with the OECD, but aim for UFA 🎯 If you align yourself with the OECD principles, you are already on the right track. But to be truly market-ready Globally, it is worth adopting the Unified Framework Approach (UFA): adopting the most stringent standard (usually the European one) as the house standard. This saves expensive "corrections" afterwards.

Quick checklist for alignment (OECD Alignment):

✅ Defining uses and risks: Who are the users? What are the prohibited/sensitive uses? ✅ Data and model: Documenting the sources of information, legal basis (Consent) and separation between training and testing sets. ✅ Pre-launch testing: Accuracy metrics, fairness tests (Bias testing) and robustness. ✅ Transparency and accountability: Clearly wording for the user when he is facing AI, how to challenge a result and who is responsible in the organization. ✅ Continuous monitoring: A channel for reporting failures and setting time points for re-testing (quarterly/semi-annually).

Bottom line: Regulatory compliance is not just a legal "headache" - it is a tool for building trust with your customers. Company A company that documents and manages risks in advance is an easier company to sell and invest in.

AI Cybersecurity Foundations

 The document 📌 "AI Cyber ​​​​Security Lays the Foundation"

is a concise, focused and practical guide that aims to lay the foundation for understanding cybersecurity in artificial intelligence (AI) systems. It is aimed at a professional audience – such as security officers (CISOs), developers, organizations and research – and provides a practical descriptive framework to address the unique security challenges of AI, such as generative models (LLM) and autonomous agents (Agentic AI).

To read or watch 👁️ click on the link 👈🏻 https://lnkd.in/dUf6-eek ✋🏻

#AI_Security_Security #AI_Security_Foundation

Prompt Injection: a simple explanation for busy people

Prompt Injection: Plain-English guide 👇

A prompt injection is when someone sneaks instructions into text that an AI model reads - causing the model to ignore its original rules and do something it shouldn’t. Think of it like a cleverly worded detour sign that makes the AI takes a wrong turn.

 (NJP 2025)

What exactly is “prompt injection”?

Prompt injection is a tactic where attackers craft input (a message, a web page, a PDF or other documents, even hidden text) that overrides the AI’s intended behavior. The model then leaks data, executes unintended actions, or produces misleading output because it treats the injected text as higher-priority instructions. This can happen with direct prompts the user types or indirect prompts buried in external content the AI ingests.

Why should an organization care?

• Data exposure: AI may reveal confidential info (PII, system prompts, credentials, source content). 

• Unauthorized actions: If the AI can call tools/APIs, injected prompts may trigger emails, file operations, or risky workflow steps. 

• Brand & compliance risk: Hallucinated or manipulated outputs can misinform customers, violate policies, or create audit findings. 

• Supply-chain knock-on effects: Compromised plugins, connectors, or data sources can propagate malicious instructions into multiple apps.

What’s the risk to an individual user?

• Privacy loss: Attackers can trick the model into recalling prior chat content or personal details the user provided. 

• Fraud & social engineering: Poisoned outputs can steer users to phishing links or bad decisions that appear “AI-approved.” 

• Reputation & errors: A junior analyst copying AI output into email or code can spread falsehoods or vulnerable snippets. 

What typically causes prompt injections?

1. Trusting user text as instructions (no separation between “data” and “directives”).
   

2. Indirect prompt sources like websites, PDFs, knowledge bases, and tickets that the AI reads automatically.

3. Insufficient output handling (treating model text as safe to render, click, or execute). 

4. Over-privileged tool access (the AI can perform powerful actions with little control). 

Fastest ways to reduce the risk (do these first)

For product owners / platform teams

• Partition “instructions” from “data.” Use strict system prompts and message roles; never let external content change the AI’s core rules. 

• Guard RAG & browsing.

  • Allow-list trusted domains and repositories.

  • Strip or neutralize markup, hidden text, and “system-like” phrases before retrieval.

  • Summarize sources rather than pasting raw content into the prompt. 

• Validate model output before acting. Treat AI text as untrusted: sanitize, escape, and require human or policy checks before any action (click, execute, send, write to DB). 

• Least privilege for tools/APIs. Scope tokens, rate-limit, add transaction guards (“are you sure?”), and require approvals for sensitive actions. 

• Detection & monitoring. Log prompts/outputs, flag patterns (e.g., “ignore previous instructions”), and red-team with known injection strings during CI/CD. 

For security & governance

• Adopt OWASP LLM Top 10 controls. Map your AI apps to LLM01 (Prompt Injection) and related risks (e.g., Sensitive Information Disclosure), then document mitigations. 

• Policy & training. Publish short usage rules: do not paste secrets, verify links, and never execute code solely because the AI suggested it. 

For end users (fast hygiene wins)

• Don’t paste sensitive data unless it’s explicitly approved.

• Be skeptical of outputs that urge urgency, secrecy, or “ignore previous instructions.”

• Confirm critical steps (money, credentials, production changes) with a second channel or a human. 

A simple mental model for juniors

• Data is not instructions. Anything the AI reads might try to boss it around.

• AI output is not truth. Treat it like a smart intern’s draft review before you act.

• Power needs brakes. The more tools the AI can use, the more guardrails you must add. 

The Bottom line

Prompt injection is LLM risk No. 1 because it exploits the very thing that makes AI useful its responsiveness to natural language. Start by separating instructions from data, treating AI output as untrusted, locking down tool access, and adopting OWASP LLM Top 10 controls. These steps deliver the fastest, most meaningful drop in risk for both organizations and individual users. 

 - - - - - - - - - - - 

FAQ

• Is this the same as “jailbreaking”?
  Related but different: jailbreaking tries to bypass safety rules via user prompts; prompt injection also includes hidden or indirect instructions from external content. 

• Can prompt injections be invisible?
  Yes. They can be embedded in code comments, HTML, PDFs, or metadata that humans might not notice - but the model parses. 

• 
  

Sources used:

1. OWASP GenAI Security Project LLM01: Prompt Injection and LLM Top 10 (2023–2025). 
2. Palo Alto Networks Cyberpedia: What Is a Prompt Injection Attack? and What Is AI Prompt Security?

Rise in AI Trends for Cyber Defense Services

The writer is a Cyber risk expert and researcher in Law and technology trends: NJ passi

The Digital Arms Race and the Need for Balance 👇

In the current digital era, where information systems are the lifeblood of businesses, governments, and critical infrastructure, cyber attackers (Black Hats) leverage artificial intelligence (AI) to enhance the efficiency of their attacks. AI-based tools enable them to identify code vulnerabilities, generate personalized attacks, and adapt strategies at an astonishing speed. However, the scalable counter-solution is the development of AI systems that empower human capabilities on the defense front: accurate vulnerability detection, high-quality fix suggestions, and acceleration of analysis processes in complex environments like Security Operations Centers (SOCs). This post, based on current trends and up-to-date research, examines how AI systems are becoming an essential tool for organizational cyber defenders. From organizational security teams to security researchers and maintainers of open-source software, as well as risk managers shaping long-term defense strategies, all require these capabilities. I will focus on the rationale visible today, with an emphasis on investments in development and their impact on the field, including changes in workforce structure in the industry, as seen in current trends.

👉 Directions of LLM Companies and Security Companies

Large AI companies (LLMs) like Anthropic are leading the shift to AI-based cyber defense, focusing on specific defensive tasks. In their latest article, Anthropic introduced Claude Sonnet 4.5, an AI model specializing in code vulnerability detection, fix creation, and network analysis, while avoiding any enhancements that favor offensive activities like writing malicious software. (https://red.anthropic.com/( The model achieves faster and more comprehensive results than humans; for example, it solved CTF (Capture-the-Flag) challenges in just 38 minutes, compared to an hour or more for human experts. The model detects new vulnerabilities in 33% of open-source code projects.

This is part of a broader trend where LLM companies are investing in defensive research to balance the advantage attackers gain from AI systems, as seen in disruptions created by Anthropic against cyber operations using AI for data fraud or espionage.

This trend is spreading to additional AI companies. For example, Google launched "A Summer of Security" in July 2025, an initiative including the Big Sleep agent for faster code vulnerability detection and the Google Unified Security platform that integrates data checking, threat intelligence, unified SOC, and AI-based automation. OpenAI, for its part, published a report in June 2025 on disruptions it created against malicious uses of its AI model, including collaboration with the U.S. Department of Defense to enhance AI capabilities in cyber defense. This defense emphasizes preventing AI exploitation by authoritarian regimes.

These companies are partners in the trend of focusing on defensive development, while integrating AI into existing tools to empower cyber defenders and information security.

At the same time, traditional security companies are integrating LLMs and AI into SOC management systems to achieve maximum control over incident analysis. For example, Palo Alto Networks completed the acquisition of IBM's QRadar SaaS assets in 2024, strengthening its Cortex XSIAM platform through integration of advanced SIEM capabilities.

This acquisition advanced SOC capabilities to address new issues like advanced AI threats and automation in threat detection, making Palo Alto a key player in the market. Not only due to internal AI development but also seamless integration with existing systems, enabling major wins already in 2025. Splunk, which currently dominates the SOC systems market as a leader in SIEM, emphasized in its State of Security 2025 report the need for a smarter SOC.

59% of organizations report that AI systems improve SOC efficiency. Along with automation of threat detection and reduction of alert fatigue states, while integrating platforms like Cisco Data Fabric. This, through machine learning integration for real-time identification of important security events. This trend is based on a practical need for AI systems that enable faster and more comprehensive analysis than a professional human and reduce incident response time by approximately 44%, in cases as examined in HackerOne cyber incidents.

👉 Use Cases - AI as a Human Empower

AI does not replace organizational cyber defenders but empowers them in specific tasks. Here are examples based on current implementations:

1. Vulnerability Detection and Fixing in Code - In the DARPA AI Cyber Challenge, teams used LLM models like Claude to analyze millions of lines of code, identify new vulnerabilities, and create fixes, including those integrated into open-source software. AI scans code at a high scale, offers precise solutions, and reduces fix time from days to just a few hours.
2. SOC Automation, Real-Time Threat Detection - CrowdStrike uses the Falcon AI platform to detect anomalous behaviors in endpoints, cloud access, and data, and responds automatically to threats. For example, it analyzes network traffic and dismantles malicious software, with a 76.5% success rate in Cybench challenges, double that of previous models. This allows SOC teams to focus on strategy instead of manual analysis.
3. Organizational Risk Management, Vulnerability Exploitation Prediction - Microsoft Security Copilot uses AI to predict which vulnerabilities will be exploited based on trends and offers tailored fixes. For open-source maintainers, Darktrace provides behavioral analysis that detects vulnerabilities in WiFi and cloud systems, while providing repair recommendations.
4. Incident Response - Triage Automation - SentinelOne integrates AI for zero-day detection and automatic response, including endpoint isolation. This reduces damage by 50% on average.

👉 Mapping of Leading Global Companies - Impact and Investments

Investments in AI for cyber defense surged in 2025, with a forecast of 5-7 trillion dollars in global economic trends. AI is becoming a leading investment target in security budgets. 74% of organizations report seeing positive impact from AI technologies in their organization (www.pwc.com). This highlights that AI systems are the top investment priority, also to address workforce shortages and increase operational efficiency. Below is a table mapping key global companies:

See table:

Company	AI Focus	Example of Impact	Investments/Trends 2025
Anthropic (Claude)	Vulnerability detection and code fixing	Partnership with CrowdStrike and HackerOne, 44% reduction in response time	Investment in defensive research, AI-based threat disruptions
Palo Alto Networks (Cortex XSIAM)	SOC automation and threat detection	QRadar SaaS acquisition, automatic alert enrichment, AI model protection	Dominance in AI-security market, 30% growth in AI investments, major SIEM wins
CrowdStrike (Falcon)	EDR and behavioral analysis	Cloud threat detection and AI workloads, 76.5% success in CTF	Native AI platform, investments in AI security on AWS
Darktrace	Behavioral analysis and prediction	Azure protection, anomaly detection in data	Leading predictive AI, partnerships with Microsoft
SentinelOne	Endpoint protection and automated response	Zero-day detection, cloud identity management	8 leading AI-security companies, growth in AI EDR
Microsoft (Security Copilot)	Risk prediction and fixing	Integration with Azure, vulnerability trend analysis	Top investment target, 55% IT efficiency improvement
Google (Big Sleep & Unified Security)	Vulnerability detection and unified SOC	"Summer of Security" initiative, AI automation	Investments in defensive AI, Growth Academy for expansion
Splunk	SIEM and smart SOC	State of Security 2025, tier-1 automation	SOC market dominance, 59% AI efficiency improvement
OpenAI	Disruption of malicious uses	June 2025 report, DoD collaboration	Focus on preventing AI threats, built-in security

Investment trends are emphasizing a shift to unified AI platforms, with a focus on SOC automation and protection of AI itself, as seen in organizations investing in customer support and IT efficiency improvements through the use of AI.

👉 Change Index - AI Efficiency vs. Humans and Workforce Structure Changes

As investments in AI development for cyber defense grow—with AI as the top budget priority—the change index becomes dramatic: 56% of organizations report improvement in threat prioritization capabilities, and 51% in enhanced SOC efficiency. AI systems are more efficient than humans in several metrics: analyzing massive data volumes in real-time (e.g., Google's Big Sleep detects vulnerabilities several times faster than a human expert), reducing human errors by 30-50%, and automating tier-1 tasks. The system responds to threats on a global scale without signs of fatigue in detection. However, AI requires human oversight for complex strategies. (https://mixmode.ai))

This change will profoundly impact the future workforce structure in cyber defense and information security departments. 52% of experts predict impact on entry-level hiring, with automation of basic tasks freeing analysts to focus on tier-2/3 (deep investigation and strategy) (www.isc2.org). Splunk reports that its SOC automated tier-1 without layoffs, but by reallocating workforce to higher-priority tasks – increasing efficiency by 43%. However, 46% of employees fear job loss, and 50% are concerned about AI accuracy risks. Organizations that invest more will see a shift to an "AI-savvy" workforce – experts combining AI with human judgment – which will reduce talent shortages by 30% and improve threat response by 55%.

👉 AI as a Partner in "Scalable Defense"

Building AI for cyber defense is not futurism; it is a current reality that balances the arms race. By empowering defenders through accurate vulnerability detection, SOC automation, and fix suggestions, we enable security teams, researchers, and risk managers to focus on implementing organizational defense strategy. The following investments in the field, around 5 trillion dollars and the expected impact, indicate acceleration in development in the field, but the emphasis must be on data-based implementations, as led by Anthropic, Google, Palo Alto, and Splunk. That is, not investing in unproven futuristic technologies, but focusing on practical applications based on real data: research, experiments, and measurable metrics.