Artificial Intelligence is no longer confined to innovation labs; it is now production-grade infrastructure powering credit underwriting, healthcare diagnostics, fraud detection, supply chain optimization, and generative enterprise copilots. As enterprises scale AI adoption, the need for advanced AI security services becomes critical to protect sensitive data, proprietary models, and distributed AI infrastructure. AI systems directly influence revenue decisions, risk exposure, regulatory standing, operational efficiency, customer trust, and brand reputation. Yet as adoption accelerates, so do the risks. AI expands the enterprise attack surface, increases regulatory complexity, and raises ethical accountability, making structured enterprise AI governance essential for long-term stability. Traditional IT security models cannot protect adaptive, data-driven systems operating across distributed environments.

To scale responsibly, organizations must implement structured and robust AI governance solutions, proactive AI risk management services, and integrated AI compliance solutions, all grounded in the principles of responsible AI development. Achieving this level of security, transparency, and regulatory alignment requires collaboration with a trusted, secure AI development company that understands the technical, operational, and compliance dimensions of enterprise AI transformation.

Why AI Introduces an Entirely New Category of Enterprise Risk ?

Artificial Intelligence is not just another layer of enterprise software; it represents a fundamental shift in how systems operate, decide, and evolve.

Traditional software systems are deterministic. They:

• Execute predefined logic
• Produce predictable, repeatable outputs
• Change only when developers modify the code

AI systems, however, operate differently. They:

• Learn patterns from historical and real-time data
• Continuously adapt through retraining
• Generate probabilistic, not guaranteed, outputs
• Process unstructured inputs such as text, images, and voice
• Evolve over time without explicit rule-based programming

This dynamic behavior introduces a new and complex category of enterprise risk.

1. Decision Risk

AI systems can produce inaccurate or biased outcomes due to flawed training data, insufficient validation, or model drift. Since decisions are probabilistic, even high-performing models can fail under edge conditions; impacting revenue, customer trust, or compliance.

2. Security Risk

AI models are high-value digital assets. They can be manipulated through adversarial attacks, extracted via repeated API queries, or compromised during training. Unlike traditional systems, AI introduces model-level vulnerabilities that require specialized protection.

3. Regulatory Risk

AI-driven decisions—particularly in finance, healthcare, insurance, and hiring—may unintentionally violate compliance regulations. Without structured oversight, organizations face legal scrutiny, fines, and operational restrictions.

4. Ethical & Reputational Risk

Biased or opaque AI decisions can trigger public backlash, regulatory investigations, and long-term brand damage. Ethical lapses in AI are not just technical failures—they are governance failures.

5. Operational Risk

AI performance can silently degrade over time due to data drift, environmental changes, or shifting user behavior. Unlike traditional systems that fail visibly, AI models may continue operating while gradually producing unreliable outputs.

Because AI systems function with varying degrees of autonomy, failures are often subtle and delayed. By the time issues surface, financial, regulatory, and reputational damage may already be significant.

This is why AI risk must be managed differently and more proactively than traditional enterprise software risk.

AI Security: Protecting Data, Models, and Infrastructure

AI security is not limited to perimeter defense or endpoint protection. It requires safeguarding the entire AI lifecycle from raw data ingestion to model deployment and continuous monitoring. Enterprise-grade AI security services are designed to protect not just systems, but the intelligence layer itself.

A secure AI architecture begins with the foundation: the data pipeline.

Layer 1: Securing the Data Pipeline

AI models depend on vast volumes of data flowing through ingestion, preprocessing, labeling, training, and storage environments. If this pipeline is compromised, the model’s integrity is compromised.

Key Threats in AI Data Pipelines

Data Poisoning: Attackers deliberately inject malicious or manipulated data into training datasets to influence model behavior, potentially embedding hidden vulnerabilities or bias.

Data Drift Manipulation: Subtle, gradual changes in incoming data can alter model outputs over time, leading to performance degradation or skewed predictions.

Unauthorized Data Access: Training datasets often include sensitive financial, healthcare, or personal information. Weak access controls can result in data breaches or regulatory violations.

Synthetic Data Injection: Maliciously generated or low-quality synthetic data may distort learning patterns and corrupt model accuracy.

Deep Mitigation Strategies

A mature AI security framework incorporates layered safeguards, including:

• End-to-end encryption for data at rest and in transit
• Secure, segmented data lakes with strict access control policies
• Dataset hashing and tamper-evident logging mechanisms
• Comprehensive data lineage tracking to trace the dataset origin and transformations
• Role-based access control (RBAC) for training and experimentation environments
• Differential privacy techniques to prevent memorization of sensitive data
• Federated learning architectures for privacy-sensitive industries

Data integrity validation is not optional; it is the bedrock of trustworthy AI. Without a secure data foundation, even the most advanced models cannot be considered reliable, compliant, or safe for enterprise deployment.

Layer 2: Model Security & Integrity Protection

While data is the foundation of AI, the model itself is the strategic core. Trained AI models represent years of research, proprietary algorithms, curated datasets, and competitive advantage. They are high-value intellectual property assets and increasingly attractive targets for cybercriminals, competitors, and malicious insiders.

Unlike traditional applications, AI models can be attacked both during training and after deployment. Securing model integrity is therefore a critical component of enterprise-grade AI risk management services.

Advanced AI Model Threats

Adversarial Attacks: These attacks introduce subtle, often imperceptible perturbations into input data, such as minor pixel modifications in images or slight token manipulation in text that cause the model to produce incorrect predictions. In high-stakes environments like healthcare or autonomous systems, such manipulations can lead to catastrophic outcomes.

Model Extraction Attacks: Attackers repeatedly query publicly exposed APIs to approximate and replicate a proprietary model’s behavior. Over time, they can reconstruct a functionally similar model, effectively stealing intellectual property without breaching internal systems directly.

Model Inversion Attacks: Through systematic querying and output analysis, attackers can infer or reconstruct sensitive data used during training posing serious privacy and regulatory risks, particularly in healthcare and finance.

Backdoor Attacks: Malicious actors may insert hidden triggers into training data. When activated by specific inputs, these triggers cause the model to behave unpredictably or maliciously while appearing normal during testing.

Prompt Injection Attacks (Large Language Models): For generative AI systems, attackers can manipulate prompts to override guardrails, extract confidential information, or bypass operational restrictions. Prompt injection is rapidly becoming one of the most exploited vulnerabilities in enterprise LLM deployments.

Enterprise-Grade Model Protection Controls

Professional AI risk management services and advanced AI security services deploy multi-layered defensive strategies, including:

• Red-team adversarial testing to simulate real-world attack scenarios
• Robustness training and gradient masking techniques to reduce model sensitivity to adversarial perturbations
• Model watermarking and fingerprinting to establish ownership and detect unauthorized duplication
• Secure API gateways with rate limiting, anomaly detection, and behavioral monitoring
• Token-level input filtering and validation in generative AI systems
• Output moderation engines to prevent unsafe or non-compliant responses
• Encrypted model storage and artifact signing to prevent tampering
• Isolated inference environments to restrict lateral movement in case of compromise

Without structured model integrity protection, AI systems remain vulnerable to exploitation, IP theft, and operational sabotage. Model security is no longer optional; it is a strategic necessity.

Layer 3: Infrastructure & MLOps Security

AI systems do not operate in isolation. They run on complex, distributed infrastructure that introduces its own set of vulnerabilities.

Enterprise AI environments typically rely on:

• High-performance GPU clusters
• Distributed containerized workloads
• Kubernetes orchestration layers
• Continuous integration and deployment (CI/CD) pipelines
• Cloud-hosted inference APIs and microservices

Each layer, if improperly configured can expose sensitive models, training data, or deployment credentials.

A mature secure AI development company integrates infrastructure security directly into AI architecture through:

• Zero-trust security models across all AI workloads and services
• Continuous container image scanning for vulnerabilities and misconfigurations
• Infrastructure-as-code (IaC) validation to detect security flaws before deployment
• Encrypted and access-controlled model registries
• Secure key management systems (KMS) for API tokens, credentials, and encryption keys
• Runtime intrusion detection and anomaly monitoring across GPU clusters and containers
• Secure multi-party computation (SMPC) or confidential computing for highly sensitive use cases

Infrastructure security must align with broader AI governance solutions and enterprise compliance requirements. AI security cannot be retrofitted after deployment. It must be engineered into development workflows, embedded into MLOps pipelines, and continuously monitored throughout the system’s lifecycle. Only when data, models, and infrastructure are secured together can AI systems operate with the level of trust required for enterprise-scale deployment.

AI Governance: Building Structured Oversight Mechanisms for Enterprise AI

As AI systems become deeply embedded in business-critical operations, governance can no longer be informal or policy-driven alone. AI governance is the structured framework that ensures AI systems operate with accountability, transparency, fairness, and regulatory alignment across their entire lifecycle.

Modern AI governance solutions go far beyond static documentation or compliance checklists. They integrate oversight directly into development pipelines, MLOps workflows, approval processes, and monitoring systems—making governance operational rather than theoretical. At the enterprise level, governance is what transforms AI from experimental technology into regulated, board-level infrastructure.

Pillar 1: Ownership & Accountability Framework

Every AI system deployed within an organization must have clearly defined ownership and control mechanisms. Without accountability, AI becomes a shadow asset; operating without oversight or traceability.

A structured enterprise AI governance framework requires:

• A clearly defined business purpose and intended use case
• Formal risk classification (low, medium, high, critical)
• A designated model owner responsible for performance and compliance
• Defined escalation authority for risk incidents or model failures
• A documented governance approval process prior to deployment

In mature governance environments, no AI system moves into production without formal compliance, risk, and ethics review.

This structured control prevents:

• Shadow AI deployments by individual departments
• Unapproved generative AI experimentation
• Regulatory blind spots
• Unmonitored third-party AI integrations

Ownership ensures responsibility. Responsibility ensures control.

Pillar 2: Explainability & Transparency Mechanisms

Explainability is no longer optional—particularly in regulated sectors such as finance, healthcare, and insurance. Regulatory bodies increasingly require organizations to justify automated decisions, especially when those decisions affect individuals’ rights, credit eligibility, employment opportunities, or medical outcomes.

To meet these expectations, organizations must embed transparency into AI architecture through:

• Model interpretability frameworks such as SHAP and LIME
• Decision traceability logs that record input-output relationships
• Version-controlled documentation of model changes
• Model cards outlining purpose, limitations, training data scope, and known risks
• Human-in-the-loop override capabilities for high-risk decisions

Transparency reduces legal exposure and strengthens stakeholder trust. When decisions can be explained and traced, enterprises are better positioned for audits, regulatory reviews, and board-level oversight. Explainability is not just a technical feature; it is a governance safeguard.

Pillar 3: Bias & Fairness Governance

AI bias represents one of the most significant ethical, reputational, and regulatory challenges in enterprise AI. Biased outcomes can lead to discrimination claims, regulatory penalties, and public backlash.

Bias can originate from multiple sources, including:

• Skewed or non-representative training datasets
• Historical discrimination embedded in legacy data
• Proxy variables that indirectly encode sensitive attributes
• Imbalanced class representation
• Inadequate validation across demographic segments

Effective AI governance solutions implement structured bias management protocols, including:

• Pre-training bias audits to assess dataset representation
• Fairness metric benchmarking (demographic parity, equal opportunity, equalized odds)
• Continuous fairness drift monitoring post-deployment
• Regular demographic impact assessments
• Threshold-based alerts for fairness deviations

Bias governance is central to responsible AI development. It ensures that AI systems align not only with performance metrics but also with societal expectations and regulatory standards. Without fairness monitoring, even technically accurate models may fail ethically and legally.

Pillar 4: Lifecycle Governance

AI governance cannot be limited to pre-deployment review. It must span the entire model lifecycle to ensure long-term reliability and compliance.

A comprehensive governance framework covers:

• Design: Risk assessment, ethical review, and use-case validation
• Data Collection: Dataset quality checks and compliance alignment
• Training: Secure model development with audit documentation
• Validation: Performance, bias, and robustness testing
• Deployment: Governance approval and secure release management
• Monitoring: Continuous drift, bias, and anomaly detection
• Retirement: Controlled decommissioning and archival documentation

Continuous lifecycle governance prevents silent model degradation, regulatory violations, and operational surprises. In high-performing enterprises, governance is not a bottleneck; it is an enabler of sustainable AI scale. By embedding structured oversight mechanisms into every stage of AI development and deployment, organizations ensure their AI systems remain secure, compliant, ethical, and aligned with strategic objectives.

AI Risk Management: From Initial Identification to Continuous Oversight

Effective AI risk management is not a one-time compliance activity, it is a structured, lifecycle-driven discipline. Professional AI risk management services implement comprehensive frameworks that govern AI systems from conception to retirement, ensuring resilience, compliance, and operational integrity.

Stage 1: Comprehensive AI Risk Identification

Every AI initiative must begin with structured risk discovery. Organizations should conduct a multidimensional evaluation that examines:

• Business impact and criticality: What operational or financial consequences arise if the model fails?
• Regulatory exposure: Does the system fall under sector-specific regulations (finance, healthcare, public sector)?
• Data sensitivity: Does the model process personally identifiable information (PII), financial records, or protected health data?
• Model autonomy level: Is the AI advisory, assistive, or fully autonomous?
• End-user exposure: Does the system directly affect customers, patients, or employees?

High-risk AI systems particularly those influencing critical decisions which require elevated scrutiny and governance controls from the outset.

Stage 2: Structured Risk Assessment & Categorization

Once risks are identified, AI systems must be classified using structured assessment frameworks. This tier-based categorization determines the depth of oversight, documentation, and control mechanisms required.

High-risk AI categories typically include:

• Credit scoring and lending decision systems
• Healthcare diagnostic and treatment recommendation models
• Insurance underwriting and claims automation engines
• Autonomous industrial and manufacturing systems
• AI systems used in public policy or critical infrastructure

These systems demand enhanced governance measures, including formal validation protocols, regulatory documentation, and executive-level oversight. Risk categorization ensures proportional governance thus allocating more stringent safeguards where impact and exposure are highest.

Stage 3: Embedded Risk Mitigation Controls

Risk mitigation must be operationalized within AI workflows not layered on as an afterthought. Mature AI risk management frameworks integrate technical and procedural safeguards such as:

• Human-in-the-loop review checkpoints for high-impact decisions
• Real-time anomaly detection systems to identify unusual behavior
• Secure retraining pipelines with validated data sources
• Documented incident response and escalation frameworks
• Access segregation and role-based permissions
• Audit trails for model updates and configuration changes

By embedding mitigation mechanisms directly into development and deployment processes, organizations reduce exposure to operational failure, regulatory penalties, and reputational damage.

Stage 4: Continuous Monitoring & Audit Readiness

AI risk is dynamic. Models evolve, data distributions shift, and regulatory landscapes change. Static governance approaches are insufficient.

Continuous monitoring frameworks include:

• Data and concept drift detection algorithms
• Performance degradation alerts and threshold monitoring
• Bias trend analysis across demographic groups
• Security anomaly detection and adversarial activity tracking
• Automated compliance reporting and audit documentation generation

This ongoing oversight transforms AI governance from reactive damage control to proactive risk anticipation.

Organizations that implement continuous monitoring achieve:

• Faster issue detection
• Reduced compliance risk
• Greater operational stability
• Stronger stakeholder trust

From Reactive Risk Management to Proactive AI Resilience

True AI risk management extends beyond compliance checklists. It builds adaptive systems capable of detecting, responding to, and learning from emerging threats.

When implemented effectively, structured AI risk management:

• Protects business continuity
• Safeguards sensitive data
• Enhances regulatory alignment
• Preserves brand reputation
• Enables responsible innovation at scale

AI risk is inevitable. Unmanaged AI risk is not.

AI Compliance: Navigating Global Regulatory Frameworks

Regulatory pressure around AI is accelerating globally. Enterprises require structured AI compliance solutions integrated into development pipelines.

EU AI Act

The EU AI Act mandates:

• • Risk classification
  • Conformity assessments
  • Transparency obligations
  • Incident reporting
  • Technical documentation

Non-compliance may result in fines up to 7% of global revenue.

U.S. AI Governance Directives

Emphasis on:

• • Algorithmic accountability
  • National security risk assessment
  • Bias mitigation
  • Model transparency

Industry-Specific Compliance

• Healthcare:
  • HIPAA compliance
  • Clinical validation protocols
• Finance:
  • Model risk management frameworks
  • Fair lending audits
• Insurance:
  • Anti-discrimination controls
• Manufacturing:
  • Autonomous system safety standards

Integrated AI compliance solutions reduce audit risk and regulatory exposure.

Responsible AI Development: Engineering Ethical Intelligence

Responsible AI development operationalizes ethical principles into enforceable technical standards.

It includes:

• Privacy-by-design architecture
• Inclusive dataset sourcing
• Clear documentation standards
• Sustainability-aware model training
• Transparent stakeholder communication
• Ethical review committees

Responsible AI improves:

• Regulatory alignment
• Customer trust
• Investor confidence
• Long-term scalability

Ethics and engineering must operate in alignment.

Why Enterprises Need a Secure AI Development Partner ?

Deploying AI at enterprise scale is no longer just a technical initiative; it is a strategic transformation that intersects cybersecurity, regulatory compliance, risk management, and ethical governance. Building secure and compliant AI systems requires deep cross-disciplinary expertise spanning data science, infrastructure security, regulatory law, model governance, and operational risk frameworks. Few organizations possess all these capabilities internally.

A strategic, secure AI development partner brings structured oversight, technical rigor, and regulatory alignment into every phase of the AI lifecycle.

Such a partner provides:

• Advanced AI security services to protect data pipelines, models, APIs, and infrastructure from evolving threats
• Structured AI governance frameworks embedded directly into development and deployment workflows
• Lifecycle-based AI risk management services covering identification, assessment, mitigation, and continuous monitoring
• Regulatory-aligned AI compliance solutions tailored to global and industry-specific mandates
• Demonstrated expertise in responsible AI development, including bias mitigation, explainability, and transparency controls

Without governance and security, AI innovation can amplify enterprise risk, exposing organizations to regulatory penalties, operational failures, intellectual property theft, and reputational damage. With the right secure AI development partner, innovation becomes structured, resilient, and strategically sustainable. AI innovation without governance increases enterprise exposure. AI innovation with governance builds long-term competitive advantage.

Trust Is the Infrastructure of AI

AI is reshaping industries at unprecedented speed, but innovation without trust creates fragility, risk, and long-term instability. Sustainable AI adoption demands more than advanced models; it requires strong foundations. Enterprises that embed robust AI security services, scalable governance frameworks, continuous risk management processes, regulatory-aligned compliance systems, and structured responsible AI practices will define the next phase of digital leadership. In the enterprise AI era, security protects innovation, governance protects reputation, compliance protects longevity, and trust protects growth. Trust is not a soft value; it is operational infrastructure. At Antier, we engineer AI systems where innovation and governance evolve together. We help enterprises scale AI securely, responsibly, and with confidence.

Hedera price has declined in recent sessions, forming a descending broadening wedge pattern that typically signals a potential bullish breakout. HBAR trades at $0.0923 at publication, remaining below the $0.0938 resistance level. 

While the technical structure suggests upside potential, Bitcoin’s direction could determine whether that breakout materializes.

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HBAR Holders Are Pulling Back On Selling

The Money Flow Index, or MFI, is forming a bullish divergence against HBAR price action. While HBAR recently posted a lower low, the MFI printed a higher reading. This divergence signals weakening selling pressure beneath the surface.

Bullish divergences often precede reversals in cryptocurrency markets. When momentum indicators improve during price declines, it reflects reduced conviction among sellers. Investors appear to be slowing distribution, which may allow HBAR to stabilize and attempt a rebound.

A confirmed breakout from the descending broadening wedge could trigger forced short liquidations. Liquidation data shows a concentration of short positions near the $0.1012 level. A move above that threshold would likely pressure bearish traders.

The liquidation map indicates most short liquidations sit at up to $0.1012. A rally through that zone could trigger approximately $4.34 million in liquidations. Forced buying from liquidated shorts often accelerates bullish momentum and strengthens breakout structures in volatile altcoins.

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Bitcoin Remains a Problem

Despite improving technical signals, Bitcoin remains the dominant influence. Hedera has shown increasing correlation with BTC over recent months. When Bitcoin declines, HBAR frequently mirrors that weakness regardless of its internal setup.

A brief divergence occurred between June and July 2025, when Bitcoin advanced while HBAR moved sideways. Outside that period, price behavior largely aligned. With correlation now stronger, HBAR could struggle if Bitcoin fails to generate upward momentum.

HBAR Price Breakout On The Cards

HBAR price sits at $0.0923, trading within the descending broadening wedge. Immediate resistance at $0.0938 continues to cap upside attempts. A confirmed breakout requires flipping $0.1005 into support and breaching $0.1071 decisively.

Clearing those levels would strengthen the bullish outlook and open the path toward $0.1300, which represents a recovery of recent losses. However, $0.1071 remains the primary short-term objective before any extended rally becomes sustainable.

Conversely, renewed Bitcoin weakness could invalidate the bullish thesis. Failure to overcome $0.0938 or loss of $0.0855 support would increase downside risk. A drop toward $0.0780 would confirm continued consolidation and delay any breakout scenario.

South Korea’s Gangnam Police Station has confirmed that 22 Bitcoins worth about ₩2.1 billion (roughly USD 1.6 million) were lost from police custody, authorities said on Friday.

The disappearance of the crypto assets, seized during an earlier investigation, was discovered during a nationwide review of virtual asset handling by law enforcement.

Seoul police lose seized Bitcoin, internal probe launched

The incident comes amid growing scrutiny of how police and prosecutors secure digital assets obtained in criminal cases, following a similar loss of 320 Bitcoin (BTC) from the Gwangju District Prosecutors’ Office last year.

Police said the 22 Bitcoin in question were voluntarily surrendered by suspects during a 2021 investigation and have been held in custody since then. During a recent internal check triggered by the Gwangju incident, investigators discovered the coins had been transferred out of the storage wallet without authorization.

Interestingly, the physical cold wallet, a USB-style device meant to securely store the private keys, was still in Gangnam Police’s possession, but the Bitcoins themselves were gone. This suggests the digital keys were accessed and the assets moved without leaving obvious signs of theft of the hardware itself.

The Gyeonggi Northern Provincial Police Agency has launched a formal internal investigation to determine exactly how the coins were transferred out and whether any personnel were involved.

So far, police have not publicly accused staff of criminal involvement, but officials said they are examining internal access logs, wallet key management procedures and any evidence of unauthorized digital transfers.

Authorities have not said whether any of the missing Bitcoin have been recovered or traced to external wallets, but investigators are reportedly reviewing blockchain transaction records.

The end of another week has arrived, which means another batch of crypto options contracts is expiring while spot markets continue to decline.

Around 38,000 Bitcoin options contracts will expire on Friday, Feb. 13, with a notional value of roughly $2.5 billion. This event is a little larger than last week’s expiry.

Crypto markets remain in bear market territory, losing around $125 billion since the start of the week, as sentiment plunges and the retail and institutional exodus continues.

Bitcoin Options Expiry

This week’s batch of Bitcoin options contracts has a put/call ratio of 0.76, meaning that there are more expiring calls (longs) than puts (shorts). Max pain is around $75,000, according to Coinglass, which is above current spot prices, so many will be out of the money on expiry.

Open interest (OI), or the value or number of Bitcoin options contracts yet to expire, remains highest at $60,000 and is now mounting up at $50,000, which has over $1 billion at these strike prices on Deribit as bearish bets increase. Total BTC options OI across all exchanges has been climbing this month and is at $36.6 billion.

Derivatives analyst ‘Laevitas’ said there was a “bear put spread” on Deribit, which involves buying a higher strike put and selling a lower strike put with the same expiry.

“With BTC stabilizing and volume cooling from panic levels, the key question is whether expiry acts as a magnet toward $75K or clears the way for the next directional move,” stated Deribit.

🚨 Options Expiry Alert 🚨

At 08:00 UTC tomorrow, over $2.9B in crypto options expire on Deribit.$BTC: $2.53B notional | Put/Call: 0.76 | Max Pain: $75K$ETH: $406M notional | Put/Call: 0.89 | Max Pain: $2,150

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After last week’s break below $70K triggered liquidations and… pic.twitter.com/ZH2dgNglrC

— Deribit (@DeribitOfficial) February 12, 2026

“Put options continue to dominate the market, with over $1 billion in BTC put options traded today, accounting for 37% of the total volume,” commented Greeks Live this week, which added that the majority of these are “out-of-the-money options priced between $60,000 and $65,000.”

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“This indicates that institutions hold a negative outlook on the medium-to-long-term market trajectory, with a strong expectation of a bearish trend within the next one to two months.”

In addition to today’s batch of Bitcoin options, around 217,000 Ethereum contracts are also expiring, with a notional value of $406 million, max pain at $2,150, and a put/call ratio of 0.89. Total ETH options OI across all exchanges is around $7 billion. This brings the total notional value of crypto options expiries to around $2.9 billion.

Spot Market Outlook

Total market capitalization is down another 1.5% on the day at $2.34 trillion as the sell-off continues. Bitcoin is weakening again, falling to just above $65,000 in late trading on Thursday and trading just above $66,000 during Friday morning’s Asian session.

Analysts are mostly bearish, with many predicting a bottom near or below its realized price of $55,000. Ether remains weak below $2,000, hitting $1,900 in an intraday low. Continued weakness for BTC will drag ETH even further down over the coming weeks.

Bitcoin (BTC) has extended its downward trajectory. Over the past 24 hours, the asset has declined 1.39%, pushing its total losses for the month beyond 30%.

While the broader bear market environment remains the primary driver of weakness, emerging on-chain signals suggest that concentrated whale activity could reportedly be amplifying BTC’s downside. 

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Whale Activity Raises Concerns Over Short-Term Bitcoin Volatility 

In a post on X (formerly Twitter), blockchain analytics firm Lookonchain reported that a whale’s (3NVeXm) deposits have coincided with Bitcoin’s price drops. Data from Arkham showed that the whale started depositing Bitcoin to Binance three weeks ago, starting out with modest amounts. 

However, activity accelerated this week. On February 11, the whale transferred 5,000 BTC into the exchange. The string of transfers has continued with the wallet sending another 2,800 coins just today.

Lookonchain suggested that the timing of these deposits may have influenced short-term price action.

“Every time he deposits BTC, the price drops. Yesterday, I warned when he made a deposit — and soon after, BTC dropped over 3%,” the post read.

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As of the latest available data, the address still holds 166.5 BTC, valued at over $11 million at current market prices. Large exchange inflows are often interpreted as a precursor to selling, as investors typically move assets to trading platforms to liquidate or hedge positions. 

While correlation does not necessarily imply causation, the scale and timing of these transfers could have increased immediate sell-side pressure in an already fragile market structure. In periods of heightened sensitivity, even the perception of whale-driven selling can amplify downside moves as traders react to on-chain signals and adjust positions accordingly.

Capitulation Signals Point to Market Stress 

The transfers come at a time of pronounced weakness across the Bitcoin market. An analyst noted that Bitcoin’s realized losses surged to $2.3 billion.

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“This puts us in the top 3-5 loss events ever recorded. Only a handful of moments in Bitcoin’s history have seen this level of capitulation,” the analysis read.

The analyst added that short-term holders, defined as those holding coins for less than 155 days, appear to be driving much of the current capitulation. Investors who accumulated BTC at $80,000-$110,000 are now locking in significant losses, suggesting that overleveraged retail participants and weaker hands are exiting their positions.

In contrast, long-term holders do not appear to be the primary source of this latest wave of selling. Historically, this cohort tends to hold through drawdowns.

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“In the past, extreme loss spikes like this triggered rebounds. We’re seeing it now: BTC bounced from $60K to $71K after the capitulation. But this could still be the beginning of a deep and slow bleed-out. Relief rallies happen even in prolonged bear markets,” the analyst stated.

Meanwhile, BeInCrypto previously highlighted several signals suggesting that BTC may still be in the early stages of a broader bear cycle, leaving room for further downside risk. CryptoQuant analysts have pointed to the $55,000 level as Bitcoin’s realized price, a level historically associated with bear market bottoms. 

In previous cycles, BTC traded 24% to 30% below its realized price before stabilizing. Currently, Bitcoin remains above that level.

When BTC approaches its realized price zone, it has historically entered a period of sideways consolidation before staging a recovery. Some analysts argue that a deeper correction toward the sub-$40,000 range could mark a more definitive bottom formation.

HONG KONG — Crypto finding a new niche as the payments tool of choice for machines, bitcoin BTC$66,560.23 not yet at rock bottom, U.S. regulatory changes and the role of prediction markets were some of the topics discussed at CoinDesk’s Consensus Hong Kong conference this week.

“As AI agents become capable of making and executing decisions independently, we may begin to see the early forms of what some call the machine economy, where AI agents can hold and transfer digital assets, pay for services and transact with one another onchain,” said Hong Kong Financial Secretary Paul Chan Mo-po.

These tools may be used to automatically book hotels and flights or make other purchases, Binance CEO Richard Teng said during a fireside chat on Thursday.

“If you think about the agentic AI, so the booking of hotels, flights, whatever purchases that you would make, how you think that those purchases will be made — it’ll be via crypto and stablecoins,” he said. “So, crypto is the currency for AI, if you think about it, and that’s how it’s going to pan out.”

Other participants discussed market volatility. Bitcoin has already fallen nearly $30,000 in a month, and some industry viewers fear it may drop further before hitting a bottom. Market participants are looking at $50,000 as one level to watch, several individuals told CoinDesk.

Similarly, the sentiment around betting markets is starting to turn negative. Traders said they were concerned the platforms might suck out liquidity from “productive sectors,” and in turn cause a “negative wealth effect.”

On the regulatory front, though Hong Kong’s policymakers’ announcements took center stage, industry participants told CoinDesk they were closely watching U.S. lawmakers and the negotiations around crypto market structure legislation.

One person said the U.S. market is large enough that it has outsize influence on other locales, and so some regulators are waiting to see how the U.S. lands before taking on policymaking in crypto.

Hong Kong does not appear to be one of these jurisdictions. The Securities and Futures Commission is moving ahead with various proposals to bring crypto companies further into the regulatory sphere.

A U.S. federal court has sentenced the chief executive of a crypto trading and multi-level marketing firm to 20 years in prison for orchestrating a massive Bitcoin-based Ponzi scheme that defrauded tens of thousands of investors worldwide.

Bitcoin Ponzi scheme CEO sentenced to 20 years

Ramil Ventura Palafox, 61, former CEO and Chairman of Praetorian Group International (PGI), received the sentence Thursday after being convicted on multiple federal charges, including wire fraud and money laundering.

According to court documents, Palafox used PGI to lure more than 90,000 investors into a purported Bitcoin (BTC) trading program that promised daily returns of 0.5% to 3%.

Prosecutors said the program never engaged in genuine trading, instead, returns were paid with funds from new investors, a classic Bitcoin Ponzi scheme.

Victims from around the world collectively invested more than $200 million, with documented losses exceeding tens of millions for many individuals.

Government filings show Palafox made lavish personal purchases with investor money, which reportedly included luxury cars, high-end designer goods and real estate. Earlier reports in the case revealed that millions flowed into personal expenses rather than investment activity, exacerbating investors’ losses.

“He spent approximately $3 million on 20 luxury vehicles, including automobiles by Porsche, Lamborghini, McClaren, Ferrari, BMW, Bentley, and others. Palafox spent approximately $329,000 on penthouse suites at a luxury hotel chain and purchased four homes in Las Vegas and Los Angeles worth more than $6 million. Palafox spent another $3 million of investors’ money to buy clothing, watches, jewelry, and home furnishings at luxury retailers, including Louboutin, Neiman Marcus, Gucci, Versace, Ferragamo, Valentino, Cartier, Rolex, and Hermes, among others,” the DoJ statement said.

Palafox initially pleaded guilty in September 2025 to fraud and money laundering charges, acknowledging his role in the Bitcoin Ponzi scheme that operated between December 2019 and October 2021.

The FBI’s Washington Field Office and IRS Criminal Investigation Division assisted in the case, and some victims have already been granted restitution orders. Efforts continue to track down remaining assets to repay those defrauded.

TLDR:

• Thailand SEC authorizes Bitcoin and digital assets as underlyings for futures and options trading
• New rules follow cabinet approval of amendments to the country’s long-standing Derivatives Act
• Trading will occur only through licensed operators on the Thailand Futures Exchange platform
• Spot crypto trading stays regulated, while payments using digital assets remain restricted

Thailand’s Securities and Exchange Commission has approved the use of Bitcoin and other digital assets in regulated derivatives markets.

Futures and options tied to crypto will trade on the Thailand Futures Exchange under licensed supervision. The move expands investor access while keeping activity inside formal rules. Spot trading remains limited to approved exchanges, and payment restrictions stay in place.

Crypto Assets Enter Thailand’s Derivatives Market

Under the revised Derivatives Act, digital assets may serve as underlying assets for futures, options, and related contracts. Bitcoin was listed among eligible instruments, alongside carbon credits and other approved assets. Trading will occur on the Thailand Futures Exchange.

The SEC stated that derivatives tied to crypto will follow the same oversight standards as traditional contracts. Operators must obtain licenses and meet reporting and compliance requirements. These controls aim to keep trading orderly and transparent.

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Thailand has regulated crypto markets since 2018. Spot trading remains allowed only through licensed exchanges. At the same time, authorities continue to prohibit the use of cryptocurrencies as everyday payment tools.

SEC Secretary-General Pornanong Budsaratragoon said the update expands investment choices and supports risk diversification. Investors can now access digital asset exposure through familiar financial products rather than direct holdings.

Framework Expands While Supervision Continues

The development gained attention on social media after Vivek Sen posted on X that Thailand was easing crypto trading rules. His post drew market interest and reflected the broader response from the crypto community.

Regulators clarified that the new structure builds on existing laws, not a full policy shift. The focus remains on controlled growth within regulated venues. Derivatives allow participation while exchanges maintain custody and compliance standards.

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The SEC also plans additional rules for operator licensing and supervision updates. Future steps may include crypto exchange-traded funds and tokenization initiatives. No timelines were provided for those measures.

Trading and settlement will follow established exchange procedures. Digital assets will function as approved underlyings rather than separate markets. Authorities said implementation will occur gradually to ensure stability.

Through these measures, Thailand expands access to crypto-based products while maintaining strict regulatory control.

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