TLDR

• Bitcoin ETFs faced a daily outflow of $410.37 million on February 12, with a cumulative net inflow of $54.31 billion.
• IBIT and FBTC experienced heavy losses, with daily outflows of $157.56M and $104.13M, respectively.
• Grayscale’s BTC ETF saw a minor outflow of $33.54 million, with a 3.25% decline in value.
• Mid-tier Bitcoin ETFs like HODL, BTCO, and BRRR also faced losses, reporting outflows and declines.
• BTCW and DEFI ETFs showed stable performance, with no inflows or outflows recorded.

According to a recent SoSoValue update on Bitcoin ETFs as of February 12, the market experienced a daily outflow of $410.37 million. Cumulative net inflow now reads at $54.31 billion with total value traded at $3.56 billion. Total net assets for Bitcoin remain solid at $82.86 billion, representing 6.34% of Bitcoin’s market cap.

Bitcoin ETFs Face Outflows as IBIT and FBTC Take Heavy Losses

Tracking the market performance of individual ETFs, the IBIT ETF, listed on NASDAQ and sponsored by BlackRock, saw a daily outflow of $157.56 million. The FBTC ETF, listed on the CBOE and sponsored by Fidelity, experienced an outflow of $104.13 million. Its daily change was a decrease of 3.25%, with a trading price of $56.91. GBTC ETF, listed on the NYSE and sponsored by Grayscale, saw a small outflow of $59.12 million.

Grayscale’s BTC ETF, listed on the NYSE, reported a minor outflow of $33.54 million. It saw a 3.25% decline in value. BITB ETF, listed on the NYSE and sponsored by Bitwise, reported a daily outflow of $7.83 million. Its cumulative net inflow is -$119.52 million. It experienced a daily decrease of 3.24%.

ARKB ETF, listed on the CBOE and sponsored by Ark & 21Shares, faced an outflow of $31.55 million. ARKB has assets totaling $1.45 billion, with a market share of 0.18%. The ETF saw a daily drop of 3.30%.

Other Mid-Tier ETFs Record Outflow While  BTCW and DEFI Maintain Stability

The HODL ETF, listed on the CBOE and sponsored by VanEck, saw an outflow of $3.24 million. It recorded a daily decrease of 3.20%, trading at $21.68. The BTCO ETF, listed on the CBOE and sponsored by Invesco, experienced a smaller outflow of $6.84 million. BTCO traded at $65.05, down 3.29% on the day.

The BRRR ETF, listed on NASDAQ and sponsored by Valkyrie, reported an outflow of $2.77 million. Its total net assets stand at $316.06 million. The ETF has a market share of just 0.03% and has declined 3.20%, trading at $18.44.

The BTCW ETF experienced stable performance, with no daily inflows or outflows, as indicated by both 1-day net inflows and cumulative net inflows. It recorded a 3.24% drop in daily value, trading at $69.19. Just like the BTCW ETF, the DEFI ETF remained stable, with no daily inflows or outflows and a cumulative net inflow.

Bitcoin price remains under pressure, down around 1.2% over the past 24 hours and trading close to $66,000 at press time. While short-term rebounds continue to appear, the broader structure still looks weak.

Now, even major institutions are turning cautious on their Bitcoin price predictions. New on-chain signals and long-term holders suggest the downside risk is not finished yet.

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Standard Chartered’s Warning Matches Weak ETF and Institutional Flows

Standard Chartered recently reiterated that Bitcoin could still fall toward $50,000 before any sustained recovery. The bank pointed to weakening ETF demand and fading institutional participation as key risks. When this view is compared with current market data, it lines up perfectly.

On the price chart, Bitcoin has broken down from a bear flag structure. A bear flag forms when prices consolidate after a sharp fall and then resume the downtrend. This pattern suggests that selling pressure remains dominant, even when short-term rebounds appear.

At the same time, institutional flow indicators are weakening. Chaikin Money Flow, or CMF, which tracks whether large capital is entering or leaving the market, has dropped sharply. CMF now looks weaker than it did during the January–April 2025 correction, when Bitcoin fell around 31%.

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This time, the decline is steeper. Bitcoin has already dropped nearly 38% from its peak, and CMF has fallen faster than in early 2025. This confirms that institutional buying is not returning yet. Without sustained inflows from large investors, rallies struggle to hold.

It is worth noting that during the April-October 2025 phase, when BTC peaked, there were only a few instances when the CMF fell under the zero line, and that too marginally. But now, the CMF dip looks way scarier.

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This is why Standard Chartered’s caution makes sense. The breakdown on the chart and weak ETF-linked flows are telling the same story. But institutional weakness is not the only concern.

On-Chain Profits and Long-Term Holders Still Point to More Downside

Beyond ETFs, on-chain data shows that investor confidence remains fragile.

One key indicator is Net Unrealized Profit and Loss, or NUPL. NUPL measures how much profit or loss holders are sitting on by comparing current prices with when coins were last moved.

During the April 2024 rebound, NUPL was near 0.42. That showed minimal unrealized profits and supported a recovery. Today, NUPL has dropped much lower. It fell to around 0.11 in early February and is now near 0.17. This means most of the leftover profits from the bull cycle have already been wiped out. But this doesn’t confirm a bottom if the bigger picture is taken into consideration.

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History shows NUPL can still fall further. In March 2023, NUPL dropped to near 0.02 when Bitcoin traded around $20,000. That marked deep capitulation before the next major rally began. Compared to that period, current NUPL levels remain relatively elevated. This suggests the market may not be fully washed out yet.

Long-term holder behavior supports this view. Long-term BTC holders are wallets that have held Bitcoin for more than one year. These investors usually accumulate during major bottoms and help stabilize prices.

Right now, they are still net sellers. In early February 2025, long-term holders reduced holdings by more than 170,000 BTC. At the peak of recent selling, in February 2026, outflows reached nearly 245,000 BTC. This is a heavier distribution than during the January–April 2025 correction.

Back then, demand from long-term holders had already started recovering before prices bounced. Today, that recovery has not appeared. In simple terms, institutions are cautious, profits are shrinking, and long-term holders are not stepping in yet. This combination makes a strong rebound unlikely in the near term.

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Why the $53,000–$48,000 Zone Still Matters on the Bitcoin Price Chart

With fundamentals and on-chain data aligned to the downside, the Bitcoin price levels now become critical.

The current bear flag projection points toward a broad support zone between $53,200 and $48,300. This range aligns with key Fibonacci retracement levels.

The midpoint of this zone sits close to $50,000, which remains a major psychological level. Round numbers often attract strong buying and selling activity, making them natural magnets during corrections. This is why Standard Chartered’s $50,000 view fits the technical structure. It is not an arbitrary target. It sits directly inside the main support band.

If selling pressure continues and ETF flows remain weak, Bitcoin could test this region in the coming months. In a deeper risk-off scenario, downside could even extend toward $42,400, which matches longer-term breakdown projections and historical support.

For this bearish Bitcoin price prediction to slow down, BTC would need to reclaim and hold above the $72,100 region with strong volume and renewed institutional inflows. That would signal that demand has returned and that the bear flag has failed. So far, there is no evidence of that.

[PRESS RELEASE – Tel Aviv, Israel, February 12th, 2026]

Playnance has expanded Be The Boss, its global partner program, through PlayW3, the Web3 social gaming platform built and operated by Playnance. The program enables individuals to launch a fully branded, fully operational Social Casino platform within minutes, with no technical setup or onboarding required. For a symbolic $1 entry, partners receive a live platform under a unique subdomain, capable of generating daily on-chain earnings and payouts through PlayW3’s infrastructure, operating on a 50/50 revshare model, which is among the highest in the industry, with daily automated on-chain payments sent directly to partners’ wallets.

More broadly, the $1 entry point reflects a growing shift in the digital economy, where platform infrastructure and distribution are no longer reserved for those with significant capital, technical resources, or development teams. Instead, digital business ownership becomes immediate, operational, and globally accessible from day one.

Unlike affiliate or referral-based models, Be The Boss provides real platform ownership rather than traffic monetization alone. Each partner, referred to as a “Boss,” operates a complete Social Casino experience powered end-to-end by Playnance’s proprietary blockchain infrastructure. Once activated, platforms go live immediately, allowing partners to focus on community growth, engagement, and distribution.

Each Boss platform also acts as a decentralized distribution node for the PlayW3 ecosystem, introducing new communities, audiences, and localized user bases into the network. As more Bosses launch and grow their platforms, the ecosystem expands organically through community-led reach rather than centralized marketing alone.

Each platform includes access to over 10,000 on-chain social casino games, alongside social prediction markets, sports-based social events, crash-style games, interactive financial markets, cash tournaments, jackpots, and built-in bonuses and retention mechanics. All technology, player support, on-chain settlement, and payouts are handled directly by Playnance via PlayW3, ensuring transparency and operational simplicity.

The Be The Boss program is already live and operating globally, with more than 2000 partners already joined and actively running platforms, and over $1.9 million paid out to Bosses to date. A $250 million partner pool has been allocated to support long-term earnings as the network expands, with each new platform strengthening network-wide reach and engagement.

Pini Peter, CEO of Playnance, said: “We believe access to digital opportunity should not be limited by capital or technical barriers. Be The Boss was built to make platform ownership accessible and practical, allowing creators and communities to operate real digital businesses from day one. What’s important is that this model is already live, operating at scale, and driven by engagement rather than hype.”

At the core of the ecosystem is G Coin, the utility token powering platform activity, rewards, and daily on-chain earnings distribution. As more Boss platforms go live and onboard new communities, activity across PlayW3 increases — driving greater usage of G Coin across gameplay, participation mechanics, and rewards. This creates a compounding economic loop where partner growth expands distribution, increased user activity drives token demand through real usage, and token-powered rewards further reinforce engagement across the network.

About Playnance

Playnance is a Web3 infrastructure and consumer platform company founded in 2020. The company develops and operates live, non-custodial, on-chain platforms designed to enable mainstream users to interact with blockchain systems through familiar Web2 experiences. Playnance focuses on reducing friction between user behavior and on-chain execution by operating consumer products at scale.

Bitcoin slipped 3% the last time this whale made a substantial deposit, is another decline on its way?

Bitcoin’s overall market state has been more than dire for the past several weeks, with the asset plummeting from over $90,000 on January 28 to its lowest position in over a year at $60,000 last Friday.

While this is a painful decline of its own, the broader market’s state has not improved much since then, and Lookonchain just published another potential sell signal.

You need to watch this whale!

Over the past 2 days, he has deposited 8,200 $BTC($559M) into #Binance.

Every time he deposits $BTC, the price drops.

Yesterday, I warned when he made a deposit — and soon after, $BTC dropped over 3%.https://t.co/8D2y9MbfFn pic.twitter.com/IyjYXvW8sx

— Lookonchain (@lookonchain) February 13, 2026

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The analytics company noted that the unknown whale had transferred 8,200 BTC (worth roughly $560 million) into Binance in the past 2 days alone.

Shortly after their previous deposit to the world’s largest exchange, the cryptocurrency’s price dipped yesterday by 3% within minutes, going from nearly $69,000 to $65,000.

In a subsequent post, Lookonchain added that the whale continued to transfer BTC to Binance, sending another batch of over 2,000 units with the likely intention to sell.

8,200 BTC to exchanges is not noise — it’s intent.

Large inflows often signal distribution or hedging.

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Liquidity tells the story before price does. 👀📊

— Global Rashid (@globalrashid007) February 13, 2026

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In contrast, Binance just completed the conversion of its entire $1 billion SAFU fund into bitcoin by purchasing roughly 15,000 BTC. Additionally, Strategy continues to make weekly acquisitions, but BTC’s price fails to rebound in a meaningful manner.

More volatility is expected later today when the US January CPI numbers are released.

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.